Vision Transformers for Breast Cancer Mammographic Image Classification

Background and Objective : The mortality rates due to breast cancer have been constantly growing and still represent one of the most common malignancies leading to death in females globally. Early and accurate detection is crucial to improve the survival rate. Recent deep learning advancements in artificial intelligence have opened a wide new avenue for further improving the results of computer-aided diagnosis. Vision transformers with their attention mechanism are among the recent promising ones, offering much-improved results for different image analysis applications, including mammography. Methods : This study investigates the application of vision transformers and attention mechanisms for mammography image categorization. In this work, we used three publicly available datasets like the Mammographic Image Analysis Society (MIAS), Curated Breast Imaging Subset of DDSM (CBIS-DDSM), and INbreast. In the preprocessing of data, augmentation is used to enhance the generalization capabilities of models, and we have applied Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve the contrast of images, especially in situations characterized by uneven lighting or low contrast levels. Results : The proposed approach demonstrated superior performance compared to traditional convolutional neural network (CNN)-based methods. In the evaluation of this vision transformer, we have obtained an accuracy of 0.99, an AUC of 0.99 and an F1 score of 0.98. Conclusion : Vision transformers and attention mechanisms have great potential to boost the detection of breast cancer using CAD systems. The findings accentuate their capability to improve the precision and reliability of mammography analysis, enabling early diagnosis and minimizing false positives and false negatives in clinical practice. The research emphasizes the need to embrace these new technologies to enhance patient outcomes and streamline healthcare resources.