  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.
