The classification of histopathological images is crucial for the early and precise detection of breast cancer. This study investigates the efficiency of deep learning models in distinguishing between Invasive Ductal Carcinoma (IDC) and non-IDC in histopathology slides. We conducted a thorough comparison examination of eight sophisticated models: ResNet-50, DenseNet-121, ResNeXt-50, Vision Transformer (ViT), GoogLeNet (Inception v3), EfficientNet, MobileNet, and SqueezeNet. This analysis was carried out using a large dataset of 277,524 image patches. Our research makes a substantial contribution to the field by offering a comprehensive assessment of the performance of each model. We particularly highlight the exceptional efficacy of attention-based mechanisms in the ViT model, which achieved a remarkable validation accuracy of 93\%, surpassing conventional convolutional networks. This study highlights the promise of advanced machine learning approaches in clinical settings, offering improved precision as well as efficiency in breast cancer diagnosis.

本研究解决了乳腺癌组织病理图像分类中的效率问题，比较了八种深度学习模型在区分侵袭性导管癌和非侵袭性导管癌的表现。研究发现，基于注意力机制的视觉变换器模型在验证准确率上达到了93%，显著优于传统卷积网络，这表明先进的机器学习方法在临床乳腺癌诊断中的潜在应用价值。

转移学习模型在乳腺癌分类中的比较分析