Wireless Capsule Endoscopy Image Classification: An Explainable AI Approach

Abstract

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

Keywords

• Deep learning
• explainable AI
• gastrointestinal diseases
• machine learning
• vision transformer
• wireless capsule endoscopy