Publication

Abstract : This paper presents an innovative method for liver tumor detection using ResNet, a deep learning architecture, with medical images as the primary data source. Following tumor detection, the methodology incorporates eXplainable AI (XAI) techniques, particularly LIME(Local Interpretable Model-agnostic Explanations), to give explanations of the model’s decision-making process. This enhances the trustworthiness of the model’s outcomes and improves its applicability in clinical settings. The proposed method reaches state-of-the-art performance in liver tumor detection and provides valuable explanations, rendering it a promising tool for medical professionals in diagnosing liver diseases.