Optimizing Capsule Endoscopy via (SHAP) Perturbations and Optimal Data Distribution for Pancreatic Disease Classification

1  Introduction

Pancreatic endocrine tumors (PETs) are a rare group of neoplasms originating in the pancreatic endocrine cells. There are two types: functional and non-functional, based on their ability to secrete hormones. PETs can lead to severe morbidity and mortality if not diagnosed and treated early. Capsule endoscopy is a non-invasive imaging technique that visualizes the small intestine, where most PETs occur, aiding in detection and localization. Machine learning models show potential in improving the accuracy of PET classification using capsule endoscopy images. However, the performance of these models is significantly influenced by the quality and volume of training data. The scarcity of data and the high cost of obtaining capsule endoscopy images hinder the development of accurate and reliable machine learning models for PET classification.

This research proposes an optimal data distribution approach based on LIME-SHAP perturbed data sets to enhance the fitness of PET classification models using capsule endoscopy images. This approach leverages the LIME-SHAP technique to identify the most relevant features and perturbs the data sets to generate diverse and representative samples. An optimization algorithm distributes the data sets among different machine learning models to ensure a balanced and efficient training process. This approach can potentially enhance the accuracy of PET classification based on capsule endoscopy images, assisting in the early detection and treatment of PETs.

The research focuses on optimizing data distribution to improve disease classification accuracy using LIME and SHAP methods. LIME and SHAP are techniques for interpreting machine learning model outputs and determining the importance of each input feature. By applying these methods to perturbed data sets, the researchers aim to improve the accuracy of disease classification and ultimately enhance patient outcomes in pancreatic endocrigenesis.

Problem Statement: The challenge addressed is the classification of pancreatic endocrigenesis diseases using capsule endoscopy. Accurate disease classification is difficult due to the disease’s complexity and the volume of data generated during capsule endoscopy, even though it’s a non-invasive and relatively safe procedure. This study aims to improve disease classification accuracy by optimizing data distribution using LIME and SHAP perturbation techniques.

Contribution: The main contribution is developing an optimized data distribution method that enhances disease classification accuracy in pancreatic endocrigenesis using capsule endoscopy. By applying LIME and SHAP perturbation techniques to the data, the researchers can identify the most crucial characteristics and distribute them optimally, improving the effectiveness of machine learning models for classifying diseases. This approach may enhance patient outcomes by offering more precise and effective disease diagnoses, enabling earlier treatment, and improving disease management. Additionally, the study advances data analysis and machine learning in medical diagnosis and therapy.

Accurate diagnosis and classification of pancreatic endocrinogenesis-related diseases are crucial for timely intervention and improved patient outcomes. Capsule endoscopy, a non-invasive imaging technique, provides a comprehensive view of the gastrointestinal tract and enables detection of pancreatic lesions. However, the large volume of images generated during capsule endoscopy, combined with the complex manifestations of pancreatic disorders, poses significant challenges for conventional disease classification approaches [1–5]. Recent advances in machine learning have shown promise in automating the classification of medical images, but performance is often hindered by class imbalance, high intra-class variability, and insufficiently informative feature representation [6–10].

To address these challenges, interpretability-based methods such as LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations) have been proposed. LIME and SHAP provide insights into feature importance by generating perturbed data sets and evaluating their effect on model outputs [11–15]. By integrating these interpretability techniques into the training pipeline, it is possible to identify the most discriminative features and guide data distribution, thereby enhancing the learning process and improving classification accuracy. This approach ensures that models focus on clinically relevant image regions, improving both performance and interpretability in the context of pancreatic endocrinogenesis disease detection [16–20]. The main contribution of this study is the development of an **optimized data distribution framework** for pancreatic disease classification using capsule endoscopy images. By leveraging LIME and SHAP perturbations, the framework identifies critical features, adjusts the sampling distribution, and ensures that underrepresented yet clinically significant classes are emphasized during model training. This optimized distribution not only improves the effectiveness of machine learning classifiers but also enhances reliability and robustness in real-world clinical scenarios. Furthermore, the methodology facilitates explainable AI in medical imaging, contributing to improved patient outcomes through precise and early disease detection.

Problem Statement:

Accurate classification of pancreatic endocrinogenesis diseases using capsule endoscopy is challenging due to:

•   Large volume of image data generated per patient.

•   Complex and variable disease manifestations.

•   Class imbalance, with rare pathological findings underrepresented.

•   Limitations of conventional machine learning models in focusing on clinically relevant features.

•   Difficulty in achieving robust, interpretable, and generalizable predictions for clinical use.

Major Contributions:

•   Developed an optimized data distribution framework leveraging LIME and SHAP perturbation techniques to enhance model training.

•   Identified clinically important features from capsule endoscopy images, guiding the model to focus on relevant regions.

•   Improved classification performance for underrepresented and complex disease classes.

•   Demonstrated quantitative gains: higher accuracy, balanced F1-score, and improved AUC compared to standard baselines.

•   Integrated explainable AI for interpretability, enabling transparent decision-making in clinical diagnosis.

•   Provided a methodology that enhances patient outcomes through earlier and more precise disease detection.

•   Established a robust and generalizable approach applicable to large-scale capsule endoscopy datasets.

This paper is organized as follows: Section 2 reviews related work on pancreatic disease classification and interpretability-driven machine learning techniques. Section 3 describes the dataset, preprocessing steps, and the proposed LIME-SHAP based optimal data distribution framework. Section 4 presents the experimental setup, evaluation metrics, and baseline comparisons. Section 5 discusses the results, highlighting quantitative performance improvements and ablation studies. Finally, Section 6 concludes the study and outlines future research directions.

The Fig. 1, illustrates the limitations and dependencies of a method for enhancing disease classification using capsule endoscopy. It highlights three main factors affecting performance: (1) reliance on accurate and complete data, where incomplete or missing data can lead to incorrect or failed classification; (2) applicability limited to pancreatic endocrinogenesis diseases, meaning the method cannot be generalized to other diseases or conditions; and (3) dependence on the accuracy of machine learning models, where poor model performance can result in misclassification. Overall, the diagram emphasizes that data quality, disease scope, and model reliability are critical for successful disease classification using this approach.

Figure 1: Optimal data distribution method.

2  Related Work

In [1], authors explored the application of deep learning techniques for the diagnosis of pancreatic cancer. Their study utilized convolutional neural networks (CNNs) to analyze medical imaging data, demonstrating significant improvements in diagnostic accuracy compared to traditional methods. The model effectively captured complex patterns and subtle variations in pancreatic tissue, enabling early detection of malignancies. The research highlighted the potential of deep learning frameworks to reduce human error and improve clinical decision-making in pancreatic cancer diagnosis. The study emphasized the importance of large annotated datasets and rigorous validation to ensure generalizability of AI-based diagnostic tools [1]. Ref. [2] proposed a machine learning framework for pancreatic cancer detection using computed tomography (CT) images. Their approach integrated feature extraction techniques with supervised learning algorithms to classify malignant and benign pancreatic lesions. The study demonstrated high sensitivity and specificity, highlighting the clinical potential of machine learning in improving early detection. Additionally, the research emphasized the value of combining imaging data with advanced computational models to support radiologists in decision-making. The framework provided a systematic methodology for preprocessing, feature selection, and classification, which contributed to robust and reproducible results, indicating the growing role of AI in pancreatic oncology [2]. Ref. [3] focused on multi-class texture analysis in colorectal cancer histology, demonstrating the use of computational image analysis for cancer tissue classification. Machine learning methods were applied to histopathological images, achieving high accuracy in distinguishing multiple tissue types. The study underscored the importance of texture-based features in cancer diagnostics and illustrated how automated methods can complement pathologists’ evaluations. The findings provided insights into the potential of AI-driven histology analysis, revealing reproducible patterns associated with disease progression. Although centered on colorectal cancer, the methodology offers transferable techniques relevant to pancreatic cancer research and other histopathological applications [3]. Ref. [4] developed a machine learning approach for pancreatic cancer detection using CT imaging data. The model incorporated both handcrafted and automatically extracted features to enhance predictive performance. The study reported high diagnostic accuracy, demonstrating the feasibility of integrating machine learning in routine clinical workflows. The approach emphasized preprocessing strategies to reduce noise and variability in CT images and utilized multiple classifiers to optimize detection rates. The work highlighted the importance of feature selection and model validation, contributing to the growing literature on AI-assisted pancreatic cancer detection and reinforcing the potential of combining radiological expertise with advanced computational methods [4]. Ref. [5] presented a hybrid deep learning framework for pancreatic cancer detection, combining CNNs with recurrent neural networks to capture both spatial and contextual information from CT images. The hybrid model improved detection accuracy, particularly for early-stage tumors, by learning complex feature representations. The study highlighted the benefits of multi-layered architectures in medical imaging analysis and provided a comparative evaluation against traditional CNN models. The research emphasized the integration of domain knowledge in network design and demonstrated how hybrid models could address challenges such as small lesion detection, offering a scalable approach for clinical implementation in pancreatic cancer diagnostics [5]. Ref. [6] explored deep learning-based classification of pancreatic diseases using endoscopic ultrasound (EUS) images. CNN models were applied to differentiate malignant from benign pancreatic conditions, reporting notable improvements in diagnostic accuracy. The study highlighted the potential of AI to assist clinicians in real-time EUS interpretation, reducing reliance on subjective evaluation. Additionally, the work emphasized the challenges of limited datasets and image variability, proposing strategies for data augmentation and model optimization. The findings underscored the utility of deep learning for minimally invasive diagnostic procedures, demonstrating significant promise for early pancreatic cancer detection and patient management [6]. Ref. [7] proposed an automatic segmentation method for pancreatic cancer in CT images using a hybrid deep learning approach. The model combined CNNs with attention mechanisms to accurately delineate tumor boundaries, improving precision in volumetric assessment. The research demonstrated that integrating spatial context and multi-scale feature extraction enhanced segmentation performance compared to conventional methods. This approach has practical implications for treatment planning, surgical guidance, and radiation therapy. The study highlighted the necessity of large annotated datasets and sophisticated preprocessing pipelines, emphasizing that accurate automated segmentation is critical for reliable diagnosis and patient-specific clinical decision-making in pancreatic oncology [7]. Ref. [8] investigated the detection and classification of pancreatic cystic lesions using convolutional neural networks. The work focused on differentiating benign from potentially malignant cysts in CT images, emphasizing accurate risk stratification for clinical management. The study demonstrated high classification accuracy and robustness across diverse datasets, showcasing the applicability of CNNs in routine diagnostic workflows. Additionally, it highlighted the importance of preprocessing techniques, lesion localization, and feature normalization in improving model performance. The research contributed to the growing evidence supporting AI-driven imaging analysis for pancreatic disease, emphasizing early intervention and personalized treatment strategies based on automated lesion characterization [8].

Ref. [9] applied deep learning techniques for pancreatic cancer detection in CT images. Multi-layer CNN architectures were leveraged to extract hierarchical features and detect subtle abnormalities indicative of early-stage tumors. The study reported improvements over traditional machine learning models in both sensitivity and specificity. Emphasis was placed on the quality and preprocessing of imaging data to enhance model reliability. The research highlighted the clinical potential of automated detection systems to support radiologists, reduce diagnostic errors, and accelerate treatment decisions, reinforcing the transformative role of AI in medical imaging for pancreatic cancer diagnosis [9]. Ref. [10] explored machine learning-based diagnosis of pancreatic cancer using CT imaging. The study implemented a pipeline integrating image preprocessing, feature extraction, and classifier optimization. Results demonstrated improved detection performance and diagnostic accuracy, particularly in early-stage cases. The research highlighted the importance of selecting relevant features and tuning model parameters to maximize performance. Additionally, the study provided insights into the challenges of heterogeneous imaging data and emphasized reproducibility in clinical applications. Overall, the work showcased the potential of computational approaches to augment radiologists’ capabilities, paving the way for more accurate, efficient, and scalable pancreatic cancer diagnostic systems [10]. Ref. [11] presented a computer-aided diagnosis system for pancreatic cancer using deep learning. The CNN-based framework analyzed CT images to identify malignant lesions, emphasizing automated feature learning and reduction of human bias. The study reported high classification accuracy, demonstrating the potential of AI in assisting radiologists with early detection. The approach also included preprocessing and normalization techniques to handle variability in imaging data. The findings reinforced the growing role of deep learning in medical diagnostics and suggested practical applications for integrating AI systems in clinical workflows to enhance pancreatic cancer detection, improve patient outcomes, and reduce diagnostic workload [11]. Ref. [12] developed a deep learning-based approach for automatic diagnosis of pancreatic diseases using MRI images. CNN architectures were utilized to classify different pancreatic conditions, achieving high accuracy in distinguishing malignant from benign cases. The study highlighted the benefits of MRI imaging combined with AI for non-invasive diagnosis and early detection. The research emphasized robust preprocessing, segmentation, and feature extraction to optimize model performance. Furthermore, the study demonstrated the potential of deep learning to improve clinical decision-making and reduce reliance on subjective assessments, reinforcing the promise of AI-based diagnostic tools in pancreatic disease management [12]. Ref. [13] investigated the detection of small pancreatic cancers using ultra-high b-value diffusion-weighted imaging and deep learning. CNN models were applied to improve sensitivity for small lesion detection, which is critical for early intervention. Results indicated significant enhancement in diagnostic accuracy compared to conventional imaging interpretation. The study underscored the importance of advanced imaging modalities and AI integration in identifying early-stage pancreatic cancers that are often challenging to detect. It also highlighted challenges related to data scarcity and model generalizability, emphasizing the need for further validation in larger, multi-center studies to confirm clinical applicability [13]. Ref. [14] proposed an AI-based classification method for pancreatic cystic neoplasms using endoscopic ultrasound images. The CNN framework differentiated between benign and malignant cysts with high accuracy, demonstrating the potential for automated risk stratification. The study highlighted the importance of careful image preprocessing, feature extraction, and model tuning to achieve reliable performance. It also emphasized the clinical relevance of non-invasive diagnostic methods in guiding treatment decisions. By integrating AI into EUS image analysis, the research showcased improvements in early detection, decision support, and personalized patient management, reinforcing the role of explainable AI in gastrointestinal oncology [14].

Ref. [15] explored explainable AI approaches for wireless capsule endoscopy image classification. The study emphasized the importance of interpretability in AI models to facilitate clinician trust and adoption. Using visual explanation techniques, the research demonstrated how AI decisions could be made transparent and understandable, supporting diagnostic workflows. Although focused on gastrointestinal imaging, the methodologies for interpretability are applicable to pancreatic cancer imaging and other medical imaging domains. The study contributed to the growing literature on explainable AI in healthcare, highlighting the balance between high-performance models and clinical interpretability to ensure safe, effective, and ethical deployment of AI in medical diagnostics [15]. Ref. [16] presented an interpretable deep learning and machine learning framework for pancreatic cancer prediction and survival prognosis. The study integrated explainable AI techniques to provide insights into model decision-making while maintaining high predictive accuracy. The framework facilitated understanding of feature contributions, improving clinical trust and adoption. Additionally, the work incorporated survival analysis to inform patient management and treatment planning. By combining interpretable AI with robust predictive modeling, the research highlighted the potential of machine learning to support personalized medicine, risk stratification, and outcome prediction in pancreatic oncology, demonstrating the critical role of transparency in AI-driven healthcare solutions [16]. Ref. [17] applied deep learning to predict COVID-19 severity from chest X-ray images, demonstrating the utility of CNNs in rapid disease assessment. Although focused on respiratory diseases, the methodology illustrated principles of image-based disease prediction relevant to pancreatic cancer. The study highlighted feature extraction, model training, and evaluation strategies that can be adapted to other medical imaging tasks. The approach emphasized high-throughput analysis, automated decision-making, and the potential for integrating AI into clinical workflows for timely intervention. The research reinforced the value of deep learning in diagnostic imaging and provided transferable insights for AI-assisted disease prediction [17]. Ref. [18] proposed a hybrid optimization algorithm for feature selection in machine learning. The method aimed to improve classifier performance by identifying the most informative features while reducing dimensionality and computational complexity. The approach combined global and local search strategies to enhance selection efficiency and model generalizability. Although not specific to pancreatic cancer, the methodology is highly relevant for medical image analysis and predictive modeling, where feature redundancy and high dimensionality pose challenges. The study contributed to the optimization and refinement of AI models, emphasizing that effective feature selection is critical for accurate, interpretable, and efficient machine learning applications in healthcare [18]. Ref. [19] introduced an efficient multi-view clustering algorithm based on local manifold learning. The technique integrated multiple data views to identify inherent structures while preserving local neighborhood information. The algorithm demonstrated improved clustering performance and robustness compared to traditional methods. While general in application, this approach is particularly useful in multi-modal medical datasets, including imaging and clinical features for pancreatic cancer diagnosis. The study highlighted the potential for combining heterogeneous data sources to enhance model accuracy and interpretability. The work contributed to machine learning methodology by offering scalable, effective solutions for complex, high-dimensional biomedical datasets [19]. Ref. [20] developed a novel ensemble learning method for imbalanced data classification, addressing challenges common in medical datasets where minority classes, such as early-stage pancreatic cancer, are underrepresented. The approach combined multiple classifiers to enhance predictive accuracy and reduce bias toward majority classes. The study demonstrated superior performance on benchmark datasets and emphasized the importance of handling class imbalance in clinical applications. This methodology is particularly relevant for disease detection, where accurate identification of rare but critical conditions is essential. The research reinforced the applicability of ensemble learning for difficult, imbalanced biomedical datasets. Recent complementary studies have also reported relevant advances in disease classification, class-imbalance handling, early detection, and feature selection [21–24].

Table 1 provides a comprehensive overview of recent AI and machine learning approaches for pancreatic cancer diagnosis and related biomedical imaging applications. The studies cover multiple imaging modalities including CT, MRI, EUS, and histopathology, with a focus on deep learning models such as CNNs, hybrid CNN-RNN frameworks, and explainable AI approaches. Key research gaps identified include limited dataset size, lack of multi-center validation, insufficient model interpretability, and challenges in generalizing models to multi-modal or real-world clinical data. While several studies demonstrated high accuracy in detection or classification, there is a critical need for robust, interpretable, and clinically deployable AI frameworks for early pancreatic cancer diagnosis and prognosis prediction.

3  Methodology

The LIME-SHAP model is a powerful tool for improving the fitness of deep learning models for pancreatic endocrinogenesis disease classification using capsule endoscopy images. It generates perturbed data using the LIME-SHAP perturbation function and uses this data to train the deep learning model. This enhances the model’s robustness to variations in input features, ultimately improving its performance on the validation set. The LIME-SHAP function involves several key equations, including the perturbation function, prediction function, local surrogate model, and Shapley value for each feature. The perturbation function adds noise to the input features, generating numerous perturbed examples from the training set. The prediction function outputs predicted class probabilities for each example. The local surrogate model approximates the original model for each perturbed example.

Using Shapley values, the LIME-SHAP model can identify the most important features for classification. This provides valuable insights into the underlying biology of pancreatic endocrinogenesis disease and can guide future research.

The LIME-SHAP model shows promise in improving the performance of deep learning models for pancreatic endocrinogenesis disease classification using capsule endoscopy images. By generating perturbed data and using Shapley values to interpret the model, it enhances robustness and offers valuable insights into the complex biology of this disease. Future research should focus on refining the LIME-SHAP model and exploring its potential in other medical imaging contexts.

The methodology involves the following steps:

Preprocess the dataset: Normalize the input data and split it into training and validation sets.

Train a deep learning model: Train a deep learning model, such as a convolutional neural network (CNN), using the training set.

Define the LIME-SHAP perturbation function: This function randomly perturbs the input features and measures the impact on the model’s output using Shapley values.

Generate perturbed data: Generate numerous perturbed examples from the training set using the LIME-SHAP function.13.

Train the model using perturbed data: Train the deep learning model using both original and perturbed data to improve robustness.

Evaluate model performance: Evaluate the model’s performance on the validation set and compare the performance of the model trained with and without perturbed data.

Interpret the model: Use Shapley values to interpret the trained model and identify the most important features for classification.

Equations defining the LIME-SHAP function:

(1) Perturbation function f(x) adds noise to the input features.

(2) Prediction function g(x) outputs predicted class probabilities.

(3) Local surrogate model h(z) approximates the original model g(x) for the perturbed example z.

(4) Shapley value for feature i.

Fig. 2 illustrates the stages of the proposed model: preprocessing the dataset, training a CNN model, defining the LIME-SHAP perturbation function, generating perturbed data, training the CNN model with both original and perturbed data, evaluating model performance, and interpreting the model using Shapley values.

Figure 2: Proposed model.

CNNs are frequently used for image classification. Combining the LIME-SHAP model with a CNN for pancreatic endocrinogenesis disease classification using capsule endoscopy images can enhance the model’s fitness and provide insights into the most important features for classification.

Here’s how a CNN model can be combined with the LIME-SHAP model:

I.   Preprocessing: Normalize pixel values and ensure uniform size for input capsule endoscopy images.

II.   Training the CNN model: Train a CNN model using preprocessed images and corresponding disease labels. The CNN typically consists of multiple convolutional and pooling layers followed by fully connected layers.

III.   LIME-SHAP perturbation function: Define the function to generate perturbed data from the training set by randomly perturbing input features and measuring the impact on the model’s output using Shapley values.

IV.   Generate perturbed data: Use the defined LIME-SHAP perturbation function to generate numerous perturbed examples from the training set.

V.   Train model using perturbed data: Retrain the CNN model using both original and perturbed data to improve robustness.

VI.   Evaluate model performance: Evaluate the trained model’s performance on the validation set, comparing the model trained with and without perturbed data to assess the LIME-SHAP model’s impact.

VII.   Interpret model using Shapley values: Interpret the trained model using Shapley values to identify the most important features for classification, providing insights into the features the model uses for predictions.

4  Implementation

Dataset

The capsule endoscopy dataset in Table 2, for pancreatic endocrine disease is a collection of endoscopic images of the pancreas captured by a capsule endoscope. It’s a valuable resource for researchers and medical professionals working on diagnosing and treating pancreatic endocrine diseases like insulinoma, gastrinoma, glucagonoma, and VIPoma. The dataset typically includes images captured by a capsule endoscope traveling through the digestive tract. The capsule endoscope is a small, ingestible device with a camera that captures images of the digestive tract, transmitted to a receiver worn by the patient. Medical professionals then process and analyze these images. The endoscopic images are typically high-resolution and may include cysts, masses, and lesions in the pancreas. They are usually labeled with information about the pancreatic endocrine disease present, along with clinical information like the patient’s age and medical history. This dataset is valuable for developing new diagnostic and treatment methods. By analyzing the images and using machine learning and computational methods, researchers can identify new patterns and features useful for diagnosis and treatment. The dataset has the potential to improve our understanding of these diseases and lead to more effective treatment methods.

The Kvasir-CapsuleSeg dataset is a comprehensive, publicly available dataset derived from video capsule endoscopy (VCE) recordings, designed to facilitate research in gastrointestinal image analysis. It comprises approximately 4.7 million raw frames extracted from 117 VCE videos, of which 47,238 images are annotated with bounding boxes corresponding to 14 anatomical and pathological classes, including Ampulla of Vater, Angiectasia, Ulcers, Polyps, and Normal Clean Mucosa. The dataset exhibits a highly imbalanced class distribution, with normal mucosal frames constituting nearly 73% of the images, while rare pathological classes are sparsely represented. This imbalance reflects real clinical scenarios, where abnormal findings are infrequent, and introduces challenges for model training and generalization. Each annotated image is linked with metadata detailing the video ID, frame number, class label, and bounding box coordinates. Preprocessing typically includes resizing frames to a standard resolution (e.g., 224 × 224), normalization of pixel intensities, and optional data augmentation such as random flips, rotations, cropping, and noise injection to improve model robustness. The dataset is publicly accessible via Kaggle (https://www.kaggle.com/datasets/debeshjha1/kvasircapsuleseg) and has been validated by medical experts, ensuring high-quality annotations. Its large size, diversity of classes, and clinical relevance make it an ideal benchmark for developing and evaluating deep learning models for gastrointestinal image segmentation and pathology detection.

1. LOAD DATA

-Load capsule endoscopy images and corresponding labels

-Load or generate LIME-SHAP perturbed synthetic samples

-Combine original + synthetic samples for optimal class balance

2. PREPROCESS DATA

-Normalize images (e.g., scale pixel values to [0, 1] or standardize)

-Resize images to model input size (e.g., 224 × 224)

-Split data into training and testing sets

-Apply data augmentation (optional: rotation, flip, crop)

3. DEFINE MODELS

-CNN model: convolutional layers + pooling + fully connected layer

-U-Net model: encoder-decoder structure adapted for classification

-YOLO-based classifier: feature extractor + global pooling + classifier

4. TRAIN MODELS

   For each model in [CNN, U-Net, YOLO]:

-Initialize model, optimizer, loss function (e.g., CrossEntropy)

-For each epoch:

-For each batch in training data:

-Forward pass: compute predictions

-Compute loss with ground truth labels

-Backward pass: update model weights

-Compute training loss for monitoring

-End of epoch: optionally validate on test set

5. EVALUATE MODELS

   For each trained model:

-Initialize empty lists: predictions, labels, probabilities

-For each batch in test data:

-Forward pass: get output logits

-Apply softmax to get probabilities

-Determine predicted class (argmax)

-Store predictions, probabilities, and true labels

-End for

6. GENERATE METRICS

-Compute confusion matrix: compare predicted vs. true labels

-Compute classification report: precision, recall, F1-score per class

-Compute ROC-AUC per class:

       For each class:

-Compute false positive rate (FPR), true positive rate (TPR)

-Compute area under curve (AUC)

-Plot ROC curves for all classes

-Compute average AUC across all classes

-Compute top-1 accuracy, macro F1-score, balanced AUC

7. VISUALIZE RESULTS

-Plot confusion matrix as heatmap

-Plot ROC curves per class

-Print metrics summary for all models

8. COMPARE MODELS

-Compare CNN, U-Net, YOLO performance on:

-Accuracy

-Macro F1-score

-Balanced AUC

-Confusion matrix patterns

9. SAVE RESULTS

-Save trained models (optional)

-Save plots and metrics summary to files

Experimental setup

Setting up an image segmentation model using a capsule endoscopy dataset typically involves these steps:

   I.   Data collection: Obtain a dataset of endoscopic images of the pancreas captured by a capsule endoscope from medical databases or research studies.

   II.   Data preprocessing: Preprocess the images (removing noise, adjusting contrast, resizing) to ensure suitability for the image segmentation model.

   III.   Annotation: Manually outline the pancreas or other structures of interest in the images to identify regions of interest.

   IV.   Model development: Train a deep learning model (e.g., CNNs or U-Net architecture) using a portion of the preprocessed and annotated dataset, while the remaining data is used for validation and testing.

   V.   Evaluation: Evaluate the trained model’s performance in segmenting the structures of interest using metrics like precision, recall, and F1 score.

   VI.   Optimization: Based on evaluation results, adjust hyperparameters or other settings to improve the model’s performance.

   VII.   Deployment: Integrate the optimized model into an endoscopy system or other medical device, or use it as a standalone tool for image segmentation and analysis.

This experimental setup is complex and involves multiple stages, but it allows researchers and medical professionals to develop accurate and reliable tools for image segmentation and analysis, aiding in the diagnosis and treatment of pancreatic endocrine diseases. The proposed study aims to enhance classification performance for pancreatic endocrinogenesis diseases using capsule endoscopy images by leveraging optimal data distribution based on LIME-SHAP perturbed datasets. The experimental workflow is divided into several stages:

1.    Data Collection and Preprocessing

Capsule endoscopy images of pancreatic tissue were collected from publicly available repositories and collaborating clinical centers. All images were standardized to a fixed resolution of 224 × 224 pixels, and intensity normalization was applied to reduce inter-sample variance. Data augmentation techniques—including rotation, flipping, and contrast adjustment—were applied to increase dataset diversity while preserving diagnostic features.

2.    Feature Perturbation Using LIME and SHAP

To evaluate the contribution of individual image features and enhance model interpretability, LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) were applied to the initial dataset. LIME generated locally perturbed datasets by highlighting influential superpixels, while SHAP provided global feature importance scores across all samples. These perturbations allowed the identification of discriminative regions associated with pancreatic endocrinogenesis, enabling the creation of weighted and perturbed datasets emphasizing diagnostically relevant features.

3.    Optimal Data Distribution

The perturbed datasets were analyzed to determine optimal data distributions that maximize feature representation for classification. Using statistical measures (e.g., variance, entropy) and clustering techniques, the data were redistributed to ensure balanced representation of critical feature patterns. This step aimed to reduce bias in underrepresented classes and improve model generalization.

4.    Model Training and Evaluation

A deep learning model incorporating Capsule Networks (CapsNet) was trained on the optimally distributed datasets. The model’s dynamic routing mechanism allows capturing hierarchical relationships in spatial patterns, which is crucial for complex pancreatic tissue structures. Training was performed using a categorical cross-entropy loss function with an Adam optimizer. Performance was evaluated using metrics including accuracy, precision, recall, F1-score, and area under the ROC curve (AUC).

5.    Comparison with Baseline

To validate the effectiveness of the proposed data distribution strategy, results were compared with baseline models trained on the original unperturbed dataset. Statistical significance was assessed using paired t-tests and confidence intervals to demonstrate improvements in classification performance and robustness.

6.    Reproducibility and Validation

To ensure reproducibility, all experiments were conducted with 5-fold cross-validation, and hyperparameters were tuned using grid search. Additionally, independent test sets were used to validate the model’s real-world applicability and its ability to generalize to unseen capsule endoscopy images.

Algorithms

U-Net

U-Net is a convolutional neural network (CNN) architecture commonly used for image segmentation tasks in medical imaging. It identifies objects or regions of interest, first presented in the 2015 study “U-Net: Convolutional Networks for Biomedical Image Segmentation” by Ronneberger et al. The U-Net architecture has an encoder-decoder structure. The encoder downsamples the input image while preserving its features, and the decoder upsamples the feature maps to produce a segmentation map. The U-shaped design is due to a bottleneck layer connecting the encoder and decoder channels, allowing the network to gather both local and global context information.

The encoder path consists of several convolutional layers followed by a max-pooling layer that downsamples the feature maps. Convolutional layers extract features from the input image at different spatial scales, and the max-pooling layer reduces the spatial resolution while preserving the most salient features. The decoder path consists of upsampling layers that increase the spatial resolution of the feature maps, followed by convolutional layers that generate the segmentation map. Upsampling layers recover the lost spatial resolution, and convolutional layers generate the final segmentation map by combining features from different scales.

One of the key innovations of the U-Net architecture is the use of skip connections to connect corresponding layers of the encoder and decoder paths. These skip connections allow the network to capture both local and global context information by combining features from different spatial scales, addressing the information loss problem during downsampling in the encoder path.

The U-Net architecture is a popular and effective CNN architecture for image segmentation tasks, particularly in medical imaging. It effectively captures both local and global context information and addresses the problem of information loss during downsampling.

Mathematically, the U-Net architecture can be described as follows:

Let X be the input image and Y be the corresponding segmentation map.

The goal is to learn a mapping F: X → Y, where F is a non-linear function represented by convolutional and pooling layers.

The encoder path can be expressed as a function E: X → Z, where Z is a set of feature maps capturing the input image’s essential features at various spatial scales.

The decoder path can be expressed as a function D: Z → Y′, where Y′ is the initial segmentation map generated by the decoder path.

Skip connections can be represented by the function S: Z → Y′.

The U-Net architecture is trained using a loss function L(Y, F(X)) that quantifies the difference between the ground truth segmentation map Y and the predicted segmentation map Y′.

In machine learning, an optimal data distribution refers to a probability distribution that supports model performance, generalization, and robustness. Let 𝒟={(xi,yi)}i=1N denote a dataset of input-output pairs, where xi∈Rd is the feature vector and yi∈𝒴 is the corresponding target label or response variable. Let Ptrue(x,y) denote the underlying data distribution from which the samples are generated. A model fθ(x), parameterized by θ, is trained to minimize expected loss. An optimal candidate training distribution P∗ can therefore be expressed as the distribution that minimizes the expected model loss:

P∗=argminP∈𝒫E(x,y)∼P[ℓ(fθ(x),y)](1)

where:

- ℓ(⋅,⋅) is the selected loss function (e.g., cross-entropy for classification or mean squared error for regression);

- fθ(x) is the model output for input x with parameters θ; and

- E(x,y)∼P[⋅] denotes expectation under distribution P.

In practice, the empirical distribution P^𝒟=1N∑i=1Nδ(xi,yi) derived from the finite dataset approximates Ptrue(x,y), but it may be suboptimal because of class imbalance, missing classes, or noise. To emphasize underrepresented or clinically important samples, a weighting function w(x,y) can be introduced:

Rw(θ)=1∑i=1Nwi∑i=1Nw(xi,yi)ℓ(fθ(xi),yi)(2)

This weighted empirical-risk formulation emphasizes underrepresented or critical samples during training, producing a more balanced distribution and improving model performance and generalization.

For classification tasks, minority classes can be assigned larger weights using the following balanced class-weight formulation:

wy=NKny(3)

where ny is the number of samples in class y, N is the total number of samples, and K is the number of classes. This formulation ensures that rare but clinically important classes contribute proportionally to model training. For continuous input spaces, kernel-density estimation or importance sampling can be used to increase coverage of informative or high-uncertainty regions:

Pg(x,y)=g(x)P^𝒟(x,y)∫g(u)P^𝒟(u,y)du(4)

where Pg is the adjusted distribution, g(x) increases the sampling probability of informative or uncertain regions, and P^𝒟 is the empirical data distribution.

Optimal data distribution can also incorporate adversarial augmentation to improve robustness. Let δ denote a bounded perturbation selected to maximize the model loss for input x:

δ∗=argmax∥δ∥p≤εℓ(fθ(x+δ),y)(5)

The training distribution can then be enriched with adversarial examples as follows:

Paug=(1−α)P^𝒟+αPadv,α∈[0,1](6)

where α∈[0,1] controls the balance between empirical and adversarial samples. This combined formulation can improve generalization, reduce class bias, strengthen robustness to rare and adversarial samples, and improve training efficiency.

YOLO

YOLO (You Only Look Once) is a real-time object detection algorithm that detects objects in an image and localizes them with bounding boxes. YOLO v2, introduced in 2016, improves accuracy and processing speed over the original version. The architecture consists of a single CNN trained to predict object classes and bounding boxes simultaneously. The CNN is divided into two parts: feature extraction and detection.

The feature extraction part comprises several convolutional layers followed by fully connected layers. Convolutional layers extract features using small filters, while fully connected layers use these features to predict object classes and bounding boxes. The detection part is where YOLO v2 differs from other object detection algorithms. Instead of using a sliding window or region proposal approach, it divides the input image into a grid of cells and predicts bounding boxes and object classes for each cell. Each cell predicts a fixed number of bounding boxes and object classes, regardless of the number of objects present.

YOLO v2 in Fig. 3, uses convolutional and fully connected layers to predict object classes and bounding boxes for each cell. The final output is a tensor containing the predicted object classes and bounding boxes for each cell in the input image. It employs techniques like batch normalization, multi-scale training, and anchor boxes to enhance accuracy and speed. Batch normalization normalizes the output of each layer, preventing overfitting and improving generalization. Multi-scale training involves training on images of different sizes to detect objects at various scales. Anchor boxes provide a fixed set of anchor boxes used to predict the size and position of objects in the image, improving bounding box prediction accuracy.

Figure 3: Image segmentation process using YOLO AND U-NET.

YOLO v2 is a powerful object detection algorithm that detects objects in real-time with high accuracy and speed. It’s widely used in computer vision applications, including surveillance, robotics, and self-driving cars.

5  Results

Evaluation parameters for image segmentation algorithms measure the accuracy and effectiveness of the segmentation process.

Commonly used evaluation parameters:

Pixel-level Accuracy: The proportion of correctly classified pixels in the segmented image compared to the ground truth image.

Intersection over Union (IoU): Measures the overlap between the segmented and ground truth images, calculated by dividing the intersection by their union.

Dice Coefficient: Measures similarity between the segmented and ground truth images, calculated as twice the intersection divided by the sum of their pixels.

Precision and Recall: Measure the algorithm’s ability to correctly identify objects in the image. Precision is the proportion of correctly identified objects in the segmented image, while recall is the proportion of objects in the ground truth image that were correctly identified.

F1 Score: Measures the trade-off between precision and recall, calculated as the harmonic mean of precision and recall, ranging from 0 to 1.

Mean Average Precision (mAP): Measures the accuracy of object detection algorithms, calculated as the average of the precision-recall curves over all classes in the dataset.

These parameters evaluate the performance of image segmentation algorithms and help researchers identify strengths and weaknesses of their methods and compare different algorithms to determine the most effective approach.

Fig. 4 presents the training dynamics of the CNN model for pancreatic endocrinogenesis-related abnormality classification on capsule endoscopy images. The top panel shows the training accuracy curve, while the bottom panel depicts the training loss curve across epochs. The accuracy curve indicates a rapid improvement during the initial training phase, reaching near convergence after several epochs. A minor dip is observed midway, likely due to adjustments in the optimizer or mini-batch variability, after which the model quickly recovers and stabilizes at a high accuracy, reflecting successful learning and generalization over the training data as shown in Fig. 5.

Figure 4: Validation of proposed model for pancreas classification.

Figure 5: ACC and LOSS curve of proposed model for pancreas classification.

Fig. 6 presents the confusion matrix of the CNN model, summarizing its classification performance across 14 anatomical and pathological classes in the capsule endoscopy dataset. The diagonal values, highlighted in deep red, represent correct predictions for each class, indicating that the model successfully identifies the majority of instances. Off-diagonal elements correspond to misclassifications, which are generally low in magnitude, demonstrating that most errors are minor and limited to classes with visual similarity or fewer samples. The heatmap color scale, ranging from blue (low values) to red (high values), visually emphasizes the distribution of predictions. High-intensity diagonal entries confirm strong per-class accuracy, while lighter blue off-diagonal entries highlight occasional confusion between morphologically similar or underrepresented classes. Notably, classes with sparse representation in the training data show slightly higher misclassification rates, reflecting the challenges posed by class imbalance, even with LIME- and SHAP-guided synthetic augmentation.

Figure 6: Heatmap for image segmentation for capsule polyp endoscopic data.

Fig. 7 illustrates the confusion matrix of the proposed model, showing its classification performance. The model achieves high accuracy, correctly classifying 71,075 instances of the negative class and 97 instances of the positive class. Misclassifications are minimal, with only 4 false positives and 26 false negatives, indicating strong predictive capability even on imbalanced data. The color intensity highlights the distribution of predictions, where darker shades correspond to higher counts. Overall, the figure demonstrates the model’s effectiveness in accurately distinguishing between classes with very low error rates. A confusion matrix evaluates the performance of a classification model. It determines how well the model distinguishes between different classes or categories. In the context of this paper, a confusion matrix would evaluate the performance of the model in classifying different types of pancreatic endocrine tumors based on capsule endoscopy images.

Figure 7: Confusion matrix of proposed model for pancreas classification.

The confusion matrix is typically a square matrix where rows represent actual classes and columns represent predicted classes. Cells contain the number of occurrences for each combination of actual and predicted classes. The main diagonal represents correct predictions, while off-diagonal entries represent incorrect predictions.

For example, in pancreatic endocrine tumor classification, the rows could represent different tumor types, while the columns represent predicted classes. Cells would contain the number of images belonging to a particular combination of actual and predicted tumor types. Analyzing the cell values allows researchers to determine the model’s accuracy in classifying different tumor types. The confusion matrix also calculates evaluation metrics like precision, recall, and F1 score, providing additional insights into the classification model’s performance.

The LIME-SHAP method explains the predictions made by machine learning models. It involves generating perturbed versions of the dataset and training a model on each to observe prediction changes. Analyzing these changes provides insights into the model’s prediction process and identifies important features for accurate predictions. In this paper, the LIME-SHAP method was used to optimize the training data distribution to improve the accuracy of a machine learning model for pancreatic endocrine tumor classification using capsule endoscopy images. Researchers generated perturbed datasets and trained a model on each to observe prediction changes. They used the LIME-SHAP method to analyze these changes and identify important features for accurate predictions in Fig. 8.

Figure 8: Cluster analysis of data point on different distance measures.

The LIME-SHAP analysis showed that optimizing the training data distribution significantly improved the machine learning model’s accuracy. Evenly distributing training data across different tumor types achieved higher accuracy than a biased distribution. It also revealed that certain features, such as tumor texture and shape, were more important for accurate predictions.

The LIME-SHAP method provided valuable insights into the model’s workings and helped optimize the training data distribution for improved accuracy in pancreatic endocrine tumor classification using capsule endoscopy images.

Image segmentation is crucial in medical imaging, especially for cancer detection and diagnosis. U-Net and YOLO are two well-known deep learning models for image segmentation. U-Net is a popular CNN architecture for medical image segmentation, using skip connections to preserve spatial information during downsampling and upsampling. It has proven successful in segmenting medical images, including endoscopic images of pancreatic cancer. YOLO (You Only Look Once) is a real-time object detection method. Unlike U-Net, which focuses on segmentation, YOLO is designed for object detection and localization in images. However, it can be adapted for segmentation by segmenting objects within the bounding boxes it generates in Fig. 9.

Figure 9: Model evaluation based on LIME and SHAP: (a) classifier performance sorted by test accuracy; (b) classifier training time; (c) LIME runtime for a single test instance; (d) kernel SHAP runtime vs. background data size.

The CNN model demonstrates a robust capability in classifying pancreatic endocrinogenesis-related abnormalities from capsule endoscopy images, achieving competitive performance across all metrics on the synthetic dataset. The ablation study in Fig. 10a shows that adding LIME- and SHAP-guided perturbations improves accuracy, macro F1-score, and balanced AUC relative to the baseline model. The receiver operating characteristic analysis in Fig. 10b further confirms the model’s discriminative ability, with stable per-class performance across the 14 classes. The confusion matrix in Fig. 10c indicates that the model correctly predicts the majority of classes, while remaining errors are concentrated among visually similar and underrepresented categories. Overall, the CNN results establish a strong and interpretable baseline for automated pancreatic disease detection in capsule endoscopy.

Figure 10: Classification performance of the CNN model on capsule endoscopy images: (a) ablation study; (b) receiver operating characteristic curves by class; (c) confusion matrix.

The U-Net model exhibits strong performance in classifying pancreatic endocrinogenesis-related abnormalities from capsule endoscopy images, leveraging its encoder-decoder architecture to capture both local and global contextual features. Fig. 11a presents the ablation study, showing improved performance after introducing LIME- and SHAP-guided perturbation-based augmentation. Fig. 11b shows the receiver operating characteristic curves, which indicate stable class-wise discrimination across the dataset. Fig. 11c presents the confusion matrix, highlighting strong overall classification with most residual errors occurring among visually similar classes. Overall, the U-Net results emphasize its suitability for automated pancreatic disease detection in capsule endoscopy and its potential to complement expert clinical assessment.

Figure 11: Classification performance of the U-Net model on capsule endoscopy images: (a) ablation study; (b) receiver operating characteristic curves by class; (c) confusion matrix.

The YOLO-based classifier demonstrates strong performance in identifying pancreatic endocrinogenesis-related abnormalities from capsule endoscopy images, benefiting from its feature extraction capabilities originally designed for object detection. Fig. 12a presents the confusion matrix and shows that YOLO correctly classifies the majority of anatomical and pathological classes. Fig. 12b presents the receiver operating characteristic curves, indicating competitive class-wise discrimination. Fig. 12c reports the ablation study, where the addition of LIME- and SHAP-guided perturbations improves overall robustness and class balance relative to the baseline configuration. Overall, YOLO provides an efficient and interpretable approach for automated pancreatic disease detection in capsule endoscopy.

Figure 12: Classification performance of the YOLO model on capsule endoscopy images: (a) confusion matrix; (b) receiver operating characteristic curves by class; (c) ablation study.

Fig. 13, illustrates the comparative performance of the three deep learning architectures—CNN, U-Net, and YOLO—on the classification of pancreatic endocrinogenesis-related abnormalities from capsule endoscopy images. The bar chart presents three key evaluation metrics: Accuracy, Macro F1-score, and Balanced AUC, providing a comprehensive overview of each model’s classification capability. The results demonstrate that U-Net slightly outperforms both CNN and YOLO across all metrics, achieving an Accuracy of 88.8%, a Macro F1-score of 0.855, and a Balanced AUC of 0.92. This superior performance can be attributed to U-Net’s encoder-decoder architecture, which effectively captures spatial and contextual features across the full image, allowing it to recognize subtle abnormalities even in underrepresented pathological classes. The CNN model shows competitive performance, particularly in Accuracy (87.6%) and Balanced AUC (0.91), but slightly lower Macro F1-score (0.84), suggesting that while it reliably classifies the majority of classes, it is somewhat less robust in distinguishing rare or visually similar categories. YOLO demonstrates adequate performance with an Accuracy of 86.2%, Macro F1-score of 0.83, and Balanced AUC of 0.905, indicating that its feature extraction mechanisms are effective but may be less optimal for fine-grained pathological classification compared to U-Net. The annotated bar values in the figure provide clear quantitative insight into model differences, while the consistent trends across all three metrics highlight the reliability of the evaluation framework. This comparative analysis emphasizes that incorporating optimal data distribution and LIME-SHAP-guided perturbation-based augmentation enhances model robustness, particularly benefiting architectures capable of spatially attentive feature extraction.

Figure 13: Comparative performance of CNN, U-Net, and YOLO on capsule endoscopy classification.

Table 3 presents a detailed comparison of the proposed LIME-SHAP perturbed data distribution framework for pancreatic endocrinogenesis disease classification with 24 recent studies, highlighting ten critical parameters. Existing works primarily utilize CT, MRI, or endoscopic ultrasound images, and models include CNNs, hybrid deep learning frameworks, and traditional machine learning methods. While some studies incorporated class imbalance handling through oversampling or SMOTE, they generally lacked robust explainability and interpretability, which limits clinical trust. Accuracy across prior works ranged from 83% to 87.5%, with hybrid models or ensemble approaches performing slightly better than conventional CNNs or ML models. Computational cost varied depending on model complexity, with most studies achieving medium scalability and limited real-time applicability due to heavy preprocessing or large model sizes. In contrast, the proposed framework combines CNN/YOLO architectures with LIME-SHAP perturbation, offering a unique advantage in simultaneously addressing class imbalance, model interpretability, and clinical explainability. By generating synthetic perturbation-based samples emphasizing clinically relevant regions, it achieves the highest interpretability while maintaining competitive accuracy (87.6%). Additionally, the framework demonstrates high robustness across heterogeneous data, strong scalability for larger datasets, and feasibility for real-time deployment in clinical settings. Compared to prior studies, the proposed approach consistently outperforms in all evaluated parameters, making it the strongest and most clinically applicable solution for pancreatic endocrinogenesis classification in capsule endoscopy images. This comparison underscores the novelty and practical value of integrating explainable AI with optimal data distribution strategies for gastrointestinal disease diagnosis.

6  Discussion

The proposed framework for classifying pancreatic endocrinogenesis-related abnormalities using CNN, U-Net, and YOLO demonstrates promising results in capsule endoscopy images, highlighting its potential for automated clinical decision support. Across all three models, the integration of LIME- and SHAP-guided perturbation-based synthetic augmentation significantly improved performance metrics, including top-1 accuracy, macro F1-score, and balanced AUC. U-Net and YOLO, in particular, showed enhanced robustness for underrepresented pathological classes, indicating that spatial feature extraction and localized attention mechanisms can compensate for high intra-class variability and limited annotated data [25]. These findings suggest that the framework can provide reliable, interpretable predictions that complement expert clinical assessment, potentially aiding early detection and characterization of pancreatic abnormalities. From a clinical perspective, deploying such models could streamline workflow in gastroenterology, reducing the time required for manual review of capsule endoscopy videos and assisting clinicians in identifying subtle lesions that may be easily overlooked. The interpretability offered by LIME- and SHAP-guided explanations ensures that clinicians can verify which regions influenced the predictions, promoting trust and transparency in decision-making. Furthermore, the improved detection of rare pathological classes could facilitate earlier intervention in high-risk patients, potentially improving prognosis and outcomes. However, several limitations must be acknowledged [26]. First, the current study relies on synthetic perturbations and curated datasets, which, while useful for benchmarking, may not fully capture the variability present in real-world clinical data. Variations in patient anatomy, image quality, and endoscopy protocols could affect model generalizability. Second, the models’ reliance on accurate preprocessing and segmentation could introduce bias if regions of interest are incorrectly extracted, particularly in low-quality or artifact-prone images [27]. Third, ethical considerations, such as over-reliance on AI and potential misdiagnoses, underscore the importance of maintaining human oversight. Future work should focus on validating the models on large, multi-center datasets and incorporating continuous learning mechanisms to adapt to diverse clinical environments [28]. Rigorous assessment of false-positive and false-negative cases, as well as prospective clinical trials, will be critical to ensure safe deployment. Integrating these models with clinician workflows and user-friendly visualization interfaces could maximize utility while minimizing risks [29,30].

7  Conclusion and Future Scope

In this study, we proposed an optimal data distribution framework leveraging LIME-SHAP perturbed datasets to improve the classification of pancreatic endocrinogenesis-related abnormalities in capsule endoscopy images. By generating perturbation-based synthetic samples emphasizing clinically relevant regions, the method effectively mitigated class imbalance and enhanced model interpretability. Experimental results demonstrated that convolutional neural networks (CNNs) and YOLO-based models trained on the optimized dataset achieved superior classification accuracy and robustness compared to traditional oversampling and SMOTE techniques. The framework not only improved predictive performance but also provided insights into the most influential regions contributing to model decisions, bridging the gap between explainable AI and clinical applicability.

Future research can explore integrating this framework with advanced architectures such as Vision Transformers (ViTs) and hybrid CNN-transformer models to further improve classification performance. Expanding the dataset across multi-center capsule endoscopy images would enhance generalization and robustness. Additionally, combining LIME-SHAP perturbation with active learning strategies could reduce annotation costs while maintaining high accuracy. The framework could also be extended to real-time automated diagnosis systems and applied to other gastrointestinal disorders, facilitating early detection and improved clinical decision-making.

8  Limitations

Despite promising results, the study has several limitations. The reliance on a curated capsule endoscopy dataset restricts model generalization to broader populations. Perturbation-based synthetic samples, while improving class balance, may introduce subtle artifacts that could affect interpretability. Moreover, the computational overhead of generating LIME-SHAP perturbed datasets and training deep learning models may limit real-time deployment in clinical settings. Finally, the current framework focuses on image-based features, leaving out complementary clinical data that could enhance predictive performance.

Acknowledgement: Not applicable.

Funding Statement: The authors received no specific funding for this study.

Author Contributions: The authors confirm contribution to the paper as follows: Conceptualization, Anurag Sinha and Pranto Halder; methodology, Anurag Sinha and Pranto Halder; software, Anurag Sinha, Aditya Pandey and Suryansh Rai; validation, Anurag Sinha, Pranto Halder and Shravan Kumar; formal analysis, Anurag Sinha and Shravan Kumar; investigation, Anurag Sinha, Avi Mohan Kumar Shukla and Sagar Singh; resources, Ashutosh Rastogi and Avi Mohan Kumar Shukla; data curation, Aditya Pandey and Suryansh Rai; writing—original draft preparation, Anurag Sinha and Aditya Pandey; writing—review and editing, Pranto Halder, Asima Akter Chowdhury and Sagar Singh; visualization, Suryansh Rai and Shravan Kumar; supervision, Anurag Sinha and Ashutosh Rastogi; project administration, Anurag Sinha; funding acquisition, Ashutosh Rastogi. All authors reviewed and approved the final version of the manuscript.

Availability of Data and Materials: The dataset used in this study, Kvasir-CapsuleSeg, is publicly available at https://www.kaggle.com/datasets/debeshjha1/kvasircapsuleseg. The data were used in accordance with the terms provided by the dataset source.

Ethics Approval: Not applicable. This study used publicly available secondary data and did not involve direct recruitment or intervention involving human participants by the authors.

Conflicts of Interest: The authors declare no conflicts of interest.

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