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。