An Explainable Deep Learning Framework for Kidney Cancer Classification Using VGG16 and Layer-Wise Relevance Propagation on CT Images

초록

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 noncancerous 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.

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