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Breast cancer is one of the leading causes of death for women worldwide.Early screening is essential for early identification, but the chance ofsurvival declines as the cancer progresses into advanced stages. For thisstudy, the most recent BRACS dataset of histological (H\&E) stained images wasused to classify breast cancer tumours, which contains both the whole-slideimages (WSI) and region-of-interest (ROI) images, however, for our study wehave considered ROI images. We have experimented using different pre-traineddeep learning models, such as Xception, EfficientNet, ResNet50, andInceptionResNet, pre-trained on the ImageNet weights. We pre-processed theBRACS ROI along with image augmentation, upsampling, and dataset splitstrategies. For the default dataset split, the best results were obtained byResNet50 achieving 66% f1-score. For the custom dataset split, the best resultswere obtained by performing upsampling and image augmentation which results in96.2% f1-score. Our second approach also reduced the number of false positiveand false negative classifications to less than 3% for each class. We believethat our study significantly impacts the early diagnosis and identification ofbreast cancer tumors and their subtypes, especially atypical and malignanttumors, thus improving patient outcomes and reducing patient mortality rates.Overall, this study has primarily focused on identifying seven (7) breastcancer tumor subtypes, and we believe that the experimental models can befine-tuned further to generalize over previous breast cancer histology datasetsas well.

Improved Breast Cancer Diagnosis through Transfer Learning on Hematoxylin and Eosin Stained Histology Images