@article{Batool2025, 
author = {Asma Batool and Fahad Ahmed and Naila Sammar Naz and Ayman Altameem and Ateeq Ur Rehman and Khan Muhammad Adnan and Ahmad Almogren},
title = {An Explainable Deep Learning Framework for Kidney Cancer Classification Using VGG16 and Layer-Wise Relevance Propagation on CT Images},
year = {2025},
journal = {Computer Modeling in Engineering & Sciences},
volume = {145},
number = {3},
pages = {4129-4152},
keywords = {Explainable artificial intelligence (XAI), deep learning, VGG16, layer-wise relevance propagation (LRP), kidney cancer, medical imaging},
url = {https://www.sciopen.com/article/10.32604/cmes.2025.073149},
doi = {10.32604/cmes.2025.073149},
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.}
}