arXiv cs.AI· Sami Alnawali, Muhammad Atif Saeed, Abdul Hadi, Azka Atiq, Akhtar Jamil, Ahmad Din·· 13 小时前AI 评分26
可解释深度学习模型用于医学影像乳腺癌诊断：十种 ImageNet 预训练模型基准测试
Interpretable Deep Learning Models for Breast Cancer Diagnosis Using Medical Imaging
AI 导读
研究在 BreakHis 组织病理数据集上按患者级划分基准测试了 ResNet50、VGG19、InceptionV3、DenseNet121、Xception。
正文 · 原文
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.
| Comments: | 37 page, 45 Figures |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.37026 [cs.CV] |
| (or arXiv:2610.37026v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.37026 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Muhammad Atif Saeed Mr. [view email]
[v1]
Tue, 27 Oct 2026 11:01:50 UTC (33,532 KB)
来源：arXiv cs.AI · arxiv.org