Wireless Capsule Endoscopy image classification: An Explainable AI approach

Deep Learning has contributed significantly to the advances made in fields of Medical Imaging and Computer Aided Diagnosis (CAD). Although a variety (DL) models exist for purposes image classification medical domain, more analysis needs be conducted on their decision-making processes. For this reason, several novel Explainable AI (XAI) techniques have been proposed recent years better understand DL models. Currently, professionals rely visual inspections diagnose potential diseases endoscopic imaging preliminary stages. However, we believe that use automated systems can enhance both efficiency such diagnoses. The aim study is increase reliability model predictions within field by implementing transfer learning balanced subset Kvasir-capsule, Wireless Capsule Endoscopy dataset. This includes top 9 classes dataset training testing. results obtained were an F1-score 97%±1% Vision Transformer model, although other as MobileNetv3Large ResNet152v2 also able achieve F1-scores over 90%. These are currently highest-reported metrics data, improving upon prior studies done same heatmaps XAI techniques, including GradCAM, GradCAM++, LayersCAM, LIME, SHAP presented form evaluated according highlighted regions importance. effort decisions top-performing look beyond black-box nature.