Interpretable Deep Learning Models for Breast Cancer Diagnosis Using Medical Imaging

2026-10-28 · ArXiv

arXiv:2610.37026v1 Announce Type: cross

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

原文链接：https://arxiv.org/abs/2610.37026

查看更多资讯请见 AI 行业资讯。