A Radiomics-based Framework for Liver Cancer Analysis using Explainable Artificial Intelligence (XAI) Methods - Anil, Bellary Chiterki; Rajkumar, Jayasimha Sondekoppa; Divya, T. L.; khaiyum, Samitha; Kiran P., Rakshitha; Ramadoss, Balakrishnan (Engineering, Technology & Applied Science Research, 2025)
This study presents a radiomics-based framework for liver cancer analysis, integrating imaging techniques with Explainable Artificial Intelligence (XAI) methods. The workflow involves collecting imaging data, extracting radiomics features to quantify tumor characteristics, and training Machine Learning (ML) models with Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) to enhance interpretability. Its results demonstrate improved predictive performance, with significant imaging biomarkers identified for disease progression and classification. The integration of XAI ensures model transparency, allowing clinicians to derive actionable insights and support personalized treatment planning. This approach aim to bridge the gap between complex algorithms and clinical decision-making, advancing liver cancer diagnosis and care.