@Article{cmes.2025.073149,
AUTHOR = {Asma Batool, Fahad Ahmed, Naila Sammar Naz, Ayman Altameem, Ateeq Ur Rehman, Khan Muhammad Adnan, Ahmad Almogren},
TITLE = {An Explainable Deep Learning Framework for Kidney Cancer Classification Using VGG16 and Layer-Wise Relevance Propagation on CT Images},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {145},
YEAR = {2025},
NUMBER = {3},
PAGES = {4129--4152},
URL = {http://www.techscience.com/CMES/v145n3/64987},
ISSN = {1526-1506},
ABSTRACT = {Early and accurate cancer diagnosis through medical imaging is crucial for guiding treatment and enhancing patient survival. However, many state-of-the-art deep learning (DL) methods remain opaque and lack clinical interpretability. This paper presents an explainable artificial intelligence (XAI) framework that combines a fine-tuned Visual Geometry Group 16-layer network (VGG16) convolutional neural network with layer-wise relevance propagation (LRP) to deliver high-performance classification and transparent decision support. This approach is evaluated on the publicly available Kaggle kidney cancer imaging dataset, which comprises labeled cancerous and non-cancerous kidney scans. The proposed model achieved 98.75% overall accuracy, with precision, recall, and F1-score each exceeding 98% on an independent test set. Crucially, LRP-derived heatmaps consistently localize anatomically and pathologically significant regions such as tumor margins in agreement with established clinical criteria. The proposed framework enhances clinician trust by delivering pixel-level justifications alongside state-of-the-art predictive performance. It facilitates informed decision-making, thereby addressing a key barrier to the clinical adoption of DL in oncology.},
DOI = {10.32604/cmes.2025.073149}
}