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

TL;DR

This study compares various deep learning architectures, including CNNs and Vision Transformer, for breast cancer detection, finding ViT models outperform CNNs with 95.15% accuracy, advancing AI-based diagnosis methods.

Contribution

It evaluates and demonstrates the superior performance of Vision Transformer models over CNNs in breast cancer histopathological image classification.

Findings

ViT models achieve 95.15% accuracy.

ViT outperforms traditional CNN architectures.

Data augmentation improves model performance.

Abstract

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.