Segmentation for mammography classification utilizing deep convolutional neural network

Mammography for the diagnosis of early breast cancer (BC) relies heavily on the identification of breast masses. However, in the early stages, it might be challenging to ascertain whether a breast mass is benign or malignant. Consequently, many deep learning (DL)-based computer-aided diagnosis (CAD) approaches for BC classification have been developed.

Recently, the transformer model has emerged as a method for overcoming the constraints of convolutional neural networks (CNN). Thus, our primary goal was to determine how well an improved transformer model could distinguish between benign and malignant breast tissues. In this instance, we drew on the Mendeley data repository's INbreast dataset, which includes benign and malignant breast types. Additionally, the segmentation anything model (SAM) method was used to generate the optimized cutoff for region of interest (ROI) extraction from all mammograms. We implemented a successful architecture modification at the bottom layer of a pyramid transformer (PTr) to identify BC from mammography images.

The proposed PTr model using a transfer learning (TL) approach with a segmentation technique achieved the best accuracy of 99.96% for binary classifications with an area under the curve (AUC) score of 99.98%, respectively. We also compared the performance of the proposed model with other transformer model vision transformers (ViT) and DL models, MobileNetV3 and EfficientNetB7, respectively.

In this study, a modified transformer model is proposed for BC prediction and mammography image classification using segmentation approaches. Data segmentation techniques accurately identify the regions affected by BC. Finally, the proposed transformer model accurately classified benign and malignant breast tissues, which is vital for radiologists to guide future treatment.