Different BI-RADS breast cancer diagnosis using MobileNetV1 and vision transformer based on explainable artificial intelligence (XAI).

ABSTRACT: Breast cancer (BC) remains one of the leading causes of death among women in the world, depending on the requirement for precise, effective, and interpretable computer-aided diagnosis systems (CADs). In this work, a hybrid deep learning (DL) framework is presented for multi-class BI-RADS BC classification using mammographic images. This framework fuses MobileNetV1, a lightweight convolutional neural network (CNN), to capture fine-grained local features and combines it with a Vision Transformer (ViT) to model global contextual connections, thereby enabling corresponding representation learning through a dual-stream structure. The evaluation was performed on the publicly available King Abdulaziz University BC Mammogram Dataset (KAUBC), which includes multi-view mammograms (craniocaudal (CC)

SUBMITTER: Abdelsabour I 

PROVIDER: S-EPMC12920763 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature