Breast cancer is one of the leading causes of death for women worldwide. Early screening is essential for early identification, but the chance of survival declines as the cancer progresses into advanced stages. For this study, the most recent BRACS dataset of histological (H\&E) stained images was used to classify breast cancer tumours, which contains both the whole-slide images (WSI) and region-of-interest (ROI) images, however, for our study we have considered ROI images. We have experimented using different pre-trained deep learning models, such as Xception, EfficientNet, ResNet50, and InceptionResNet, pre-trained on the ImageNet weights. We pre-processed the BRACS ROI along with image augmentation, upsampling, and dataset split strategies. For the default dataset split, the best results were obtained by ResNet50 achieving 66\% f1-score. For the custom dataset split, the best results were obtained by performing upsampling and image augmentation which results in 96.2\% f1-score. Our second approach also reduced the number of false positive and false negative classifications to less than 3\% for each class. We believe that our study significantly impacts the early diagnosis and identification of breast cancer tumors and their subtypes, especially atypical and malignant tumors, thus improving patient outcomes and reducing patient mortality rates. Overall, this study has primarily focused on identifying seven (7) breast cancer tumor subtypes, and we believe that the experimental models can be fine-tuned further to generalize over previous breast cancer histology datasets as well.

通过使用最新的BRACS组织学（H＆E染色）图像数据集中的感兴趣区域（ROI）图像，我们使用不同的预训练深度学习模型，在处理图像时应用了数据增强、上采样和数据集拆分策略，最终达到了96.2％的f1-score，显著提高了乳腺癌肿瘤的早期诊断和鉴别，尤其是不典型和恶性肿瘤，从而改善患者预后并降低患者死亡率。

通过对血红蛋白和嗜酸性染色组织学图像进行迁移学习改进乳腺癌诊断