@Article{jimh.2026.075373,
AUTHOR = {Anurag Sinha, Pranto Halder, Aditya Pandey, Avi Mohan Kumar Shukla, Shravan Kumar, Ashutosh Rastogi, Asima Akter Chowdhury, Suryansh Rai, Sagar Singh},
TITLE = {Optimizing Capsule Endoscopy via (SHAP) Perturbations and Optimal Data Distribution for Pancreatic Disease Classification},
JOURNAL = {Journal of Intelligent Medicine and Healthcare},
VOLUME = {4},
YEAR = {2026},
NUMBER = {1},
PAGES = {125--153},
URL = {http://www.techscience.com/JIMH/v4n1/68748},
ISSN = {2837-634X},
ABSTRACT = {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.},
DOI = {10.32604/jimh.2026.075373}
}