LEVERAGING TRANSFER LEARNING FOR ENHANCED BREAST CANCER DETECTION WITH VISION TRANSFORMERS

Abstract: Breast cancer continues to be a leading cause of mortality among women worldwide, necessitating early and precise diagnostic systems. While Convolutional Neural Networks (CNNs) have made significant strides in medical image analysis, their limitations in modeling long-range dependencies persist. This study proposes an advanced breast cancer detection model based on Vision Transformers (ViTs) integrated with transfer learning. Pre-trained ViT models were fine-tuned on histopathological breast cancer image datasets to address data scarcity and enhance classification accuracy. The model was evaluated using metrics such as accuracy, AUC, and F1-score, and showed superior performance compared to traditional CNNs. These results highlight the potential of ViTs in transforming breast cancer diagnosis into a more automated, robust, and accurate process.

Keywords: Breast Cancer Detection, Vision Transformers, Transfer Learning, Medical Image Analysis, Deep Learning, Histopathology