Breast Cancer Detection and Diagnosis: A comparative study of
  state-of-the-arts deep learning architectures
Brennon Maistry Absalom E. Ezugwu
Breast cancer is a prevalent form of cancer among women, with over 1.5
million women being diagnosed each year. Unfortunately, the survival rates for
breast cancer patients in certain third-world countries, like South Africa, are
alarmingly low, with only 40% of diagnosed patients surviving beyond five
years. The inadequate availability of resources, including qualified
pathologists, delayed diagnoses, and ineffective therapy planning, contribute
to this low survival rate. To address this pressing issue, medical specialists
and researchers have turned to domain-specific AI approaches, specifically deep
learning models, to develop end-to-end solutions that can be integrated into
computer-aided diagnosis (CAD) systems. By improving the workflow of
pathologists, these AI models have the potential to enhance the detection and
diagnosis of breast cancer. This research focuses on evaluating the performance
of various cutting-edge convolutional neural network (CNN) architectures in
comparison to a relatively new model called the Vision Trans-former (ViT). The
objective is to determine the superiority of these models in terms of their
accuracy and effectiveness. The experimental results reveal that the ViT models
outperform the other selected state-of-the-art CNN architectures, achieving an
impressive accuracy rate of 95.15%. This study signifies a significant
advancement in the field, as it explores the utilization of data augmentation
and other relevant preprocessing techniques in conjunction with deep learning
models for the detection and diagnosis of breast cancer using datasets of
Breast Cancer Histopathological Image Classification.