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arXiv AI By Sami Alnawali, Muhammad Atif Saeed, Abdul Hadi, Azka Atiq, Akhtar Jamil, Ahmad Din

Interpretable Deep Learning Models for Breast Cancer Diagnosis Using Medical Imaging

Read the original on arXiv AI →

The study evaluates ten ImageNet‑pretrained deep learning models for classifying benign versus malignant breast cancer in the BreakHis histopathology dataset, using a patient‑level split. DenseNet121 achieved the highest performance with 99.4% accuracy and an AUC‑ROC of 0.999, outperforming all other models (p < 0.001). Explainability methods (Grad‑CAM, LIME, Integrated Gradients, LRP) consistently identified nuclei‑rich and glandular regions as key diagnostic features, demonstrating that transfer learning combined with explainable AI can produce accurate and interpretable breast cancer diagnostic tools.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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