TY  - EJOU
AU  - Sinha, Anurag 
AU  - Halder, Pranto 
AU  - Pandey, Aditya 
AU  - Shukla, Avi Mohan Kumar 
AU  - Kumar, Shravan 
AU  - Rastogi, Ashutosh 
AU  - Chowdhury, Asima Akter 
AU  - Rai, Suryansh 
AU  - Singh, Sagar 

TI  - Optimizing Capsule Endoscopy via (SHAP) Perturbations and Optimal Data Distribution for Pancreatic Disease Classification
T2  - Journal of Intelligent Medicine and Healthcare

PY  - 2026
VL  - 4
IS  - 1
SN  - 2837-634X

AB  - Accurate classification of pancreatic endocrinogenesis-related abnormalities in capsule endoscopy images remains challenging because of class imbalance, high intra-class variability, and limited annotated data. This study proposes an optimal data distribution framework based on Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) perturbations to enhance automated classification performance. The workflow combines perturbation-guided data augmentation, feature-importance analysis, and deep-learning classifiers, including convolutional neural networks (CNNs), U-Net, and You Only Look Once (YOLO). The approach is evaluated on the Kvasir-CapsuleSeg dataset using accuracy, macro F1-score, balanced area under the receiver operating characteristic curve (AUC), and confusion-matrix analysis. Results indicate that perturbation-guided augmentation improves robustness and class balance, with consistent gains across models and particularly strong performance for underrepresented classes. These findings suggest that explainability-driven data optimization can improve automated capsule endoscopy analysis and support more interpretable pancreatic disease classification.
KW  - Optimal data distribution; local interpretable model-agnostic explanations; SHapley Additive exPlanations; pancreatic disease classification; capsule endoscopy; explainable artificial intelligence; image analysis

DO  - 10.32604/jimh.2026.075373