EMViT-BCC: Enhanced Mobile Vision Transformer for Breast Cancer Classification

Abstract

Breast cancer (BC) accounts for most cancer-related deaths worldwide, so it is crucial to consider it as a prominent issue and emphasize proper diagnosis and timely detection. This study introduces a deep learning strategy called EMViT-BCC for the BC histopathology image classification to two class and eight class. The proposed model utilizes the Mobile Vision Transformer (MobileViT) block, which captures local and global features and extracts necessary features for the classification task. The proposed approach is trained and evaluated on the standard BreaKHis dataset. The model is evaluated with both the original raw histopathology images as well as the stain-normalized images for the analysis of the classification task. Extensive experiments demonstrate that the proposed EMViT-BCC achieves higher accuracy and robustness in classifying benign and malignant images and identifying various subtypes of BC. Our results demonstrate that by incorporating further layers, the classification performance of MobileViT can be greatly enhanced, with 99.43% for two-class and 93.61% for eight-class classification. These findings suggest that while stain normalization can standardize variations, original image data retain crucial details that enhance model performance. In comparison with the existing works, the proposed methodology surpasses the state-of-the-art (SOTA) methods for BC histopathology image classification. The proposed approach offers a promising solution for reliable BC classification for both binary and multi-class.