Magnification Independent Breast Cancer Analysis Using Vision Transformer

Breast Cancer (BC) remains a critical global health challenge, accounting for millions of lives annually. Most breast cancer detection techniques are designed for binary classification. However, further classifying these types into their subtypes remains challenging due to cell coherence, variations in colour distribution, and intraclass similarity. Additionally, these techniques are limited to specific magnification levels of input images. As a result, the need to develop multiclass classification techniques independent of magnification levels has become increasingly demanding. This research introduces an innovative approach based on an enhanced vision transformer (ViT) for BC analysis that seamlessly handles binary and multiclass classification tasks in a magnification-independent manner. The proposed framework incorporates convolutional layers into the ViT model to simultaneously enhance its capability to capture local and global features. This innovative adjustment also empowers the model to capture complex patterns and subtle features by overcoming the issue of different magnification levels. Extensive experiments are conducted on the benchmark BreaKHis dataset to validate the effectiveness of the proposed framework. It achieves an accuracy of 89.43% for binary classification (benign vs. malignant) and 57.94% and 74.71% for multiclass benign and multiclass malignant subclass classification, respectively. The results confirm that the proposed framework surpasses the performance of the original ViT model and other state-of-the-art techniques.