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Breast cancer is a prevalent form of cancer among women, with over 1.5million women being diagnosed each year. Unfortunately, the survival rates forbreast cancer patients in certain third-world countries, like South Africa, arealarmingly low, with only 40% of diagnosed patients surviving beyond fiveyears. The inadequate availability of resources, including qualifiedpathologists, delayed diagnoses, and ineffective therapy planning, contributeto this low survival rate. To address this pressing issue, medical specialistsand researchers have turned to domain-specific AI approaches, specifically deeplearning models, to develop end-to-end solutions that can be integrated intocomputer-aided diagnosis (CAD) systems. By improving the workflow ofpathologists, these AI models have the potential to enhance the detection anddiagnosis of breast cancer. This research focuses on evaluating the performanceof various cutting-edge convolutional neural network (CNN) architectures incomparison to a relatively new model called the Vision Trans-former (ViT). Theobjective is to determine the superiority of these models in terms of theiraccuracy and effectiveness. The experimental results reveal that the ViT modelsoutperform the other selected state-of-the-art CNN architectures, achieving animpressive accuracy rate of 95.15%. This study signifies a significantadvancement in the field, as it explores the utilization of data augmentationand other relevant preprocessing techniques in conjunction with deep learningmodels for the detection and diagnosis of breast cancer using datasets ofBreast Cancer Histopathological Image Classification.

Breast Cancer Detection and Diagnosis: A comparative study of state-of-the-arts deep learning architectures