Fine-tuned Transformer Models for Breast Cancer Detection and Classification

TL;DR

This paper evaluates various visual transformer models for breast cancer detection using mammographic images, demonstrating high accuracy and highlighting the potential of AI to improve early diagnosis, despite challenges with dataset diversity.

Contribution

It introduces the application of multiple transformer architectures for breast cancer detection, showing their effectiveness over traditional CNNs in medical imaging analysis.

Findings

ViT achieved 99.32% accuracy in detection

Data augmentation improved model performance

Transformer models show promise for early breast cancer diagnosis

Abstract

Breast cancer is still the second top cause of cancer deaths worldwide and this emphasizes the importance of necessary steps for early detection. Traditional diagnostic methods, such as mammography, ultrasound, and thermography, which have limitations when it comes to catching subtle patterns and reducing false positives. New technologies like artificial intelligence (AI) and deep learning have brought about the revolution in medical imaging analysis. Nevertheless, typical architectures such as Convolutional Neural Networks (CNNs) often have problems with modeling long-range dependencies. It explores the application of visual transformer models (here: Swin Tiny, DeiT, BEiT, ViT, and YOLOv8) for breast cancer detection through a collection of mammographic image sets. The ViT model reached the highest accuracy of 99.32% which showed its superiority in detecting global patterns as well as subtle image features. Data augmenting approaches, such as resizing croppings, flippings, and normalization, were further applied to the model for achieving higher performance. Although there were interesting results, the issues of dataset diversity and model optimization which present new avenues of research are also still present. Through this study, the crystal potential of transformer-based AI models in changing the detecting process of breast cancer and, thus, to patients health, is suggested.