Wireless Capsule Endoscopy Image Classification: An Explainable AI Approach

This study focuses on improving the accuracy of automated diagnoses in medical imaging, specifically endoscopic images, using advanced deep learning models. We aim to enhance the understanding of these models’ decision-making processes by employing Explainable AI (XAI) techniques. Our research uses a subset of the Kvasir-capsule dataset, a Wireless Capsule Endoscopy imaging dataset, concentrating on the top 9 classes for training and testing.MobileNetv3Large is a convolutional neural network (CNN) architecture specifically designed for mobile and edge devices. It is an evolution of the MobileNet architecture, optimized for efficient and lightweight model inference.ResNet152v2 is part of the ResNet (Residual Network) family, known for its deep architecture with residual connections. ResNet152v2 is an extension with 152 layers. These scores surpass previous studies on the same dataset, indicating improved diagnostic reliability. To unravel the decision-making of these models, we utilized XAI techniques, including presenting heat maps that highlight important regions in the images. This effort aims to provide insights into the top-performing deep learning models and demystify their complex decision processes summary, our research not only achieves improved diagnostic reliability but also contributes to the interpretability of deep learning models in the medical imaging domain. The use of Explainable AI techniques allows us to peek into the decision processes of these models, making them more transparent and trustworthy for medical professionals.