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arXiv · 2610.37026

Interpretable Deep Learning Models for Breast Cancer Diagnosis Using Medical Imaging

Sami Alnawali·Muhammad Atif Saeed·Abdul Hadi·Azka Atiq·Akhtar Jamil·Ahmad Din
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Abstract

Deep learning shows promise for breast cancer diagnosis, but limited interpretability hinders clinical adoption. We benchmark ten ImageNet-pretrained models (ResNet50, VGG19, InceptionV3, DenseNet121, Xception, MobileNetV2, EfficientNet, ConvNeXt, ViT, and Swin Transformer) for benign-versus-malignant classification on the BreakHis histopathology dataset using a patient-level split, and explain their predictions with Grad-CAM, LIME, Integrated Gradients, and LRP. DenseNet121 performed best, with 99.4% test accuracy and an AUC-ROC of 0.999, and significantly outperformed all other models (p < 0.001). Transformer-based models trailed the CNNs. The explanations consistently highlighted nuclei-rich and glandular regions of diagnostic relevance. Combining transfer learning with explainable AI can thus yield accurate, interpretable tools for breast cancer diagnosis.

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Sami Alnawali, Muhammad Atif Saeed, Abdul Hadi, Azka Atiq, Akhtar Jamil, Ahmad Din. 2026-10-27. Interpretable Deep Learning Models for Breast Cancer Diagnosis Using Medical Imaging. https://arxiv.org/abs/2610.37026

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