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

## TL;DR

This paper proposes a new AI system combining MobileNetV1 and Vision Transformer for accurate and interpretable breast cancer diagnosis using mammograms.

## Contribution

A novel dual-stream framework using MobileNetV1 and ViT with XAI for multi-class BI-RADS classification of breast cancer.

## Key findings

- The proposed framework achieved over 99% accuracy, sensitivity, and specificity across all BI-RADS categories.
- XAI techniques like Grad-CAM provided interpretable visual explanations for AI-based diagnoses.
- The model outperformed existing CNN and transformer models in multi-class BC classification.

## 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) and mediolateral oblique (MLO)) arranged according to the BI-RADS classification scheme and characterized by class imbalance. Feature-level fusion is performed, followed by a bagging-based logistic regression (LR) classifier to enhance robustness and decrease prediction conflict. The proposed approach was extensively analyzed using 5-fold cross-validation and compared with multiple state-of-the-art CNN and transformer models, each fused with various machine learning (ML) classifiers. The experimental results demonstrate higher and stable performance across all BI-RADS categories, with accuracy (ACC), sensitivity (SEN), and specificity (SPE) exceeding 99%. In addition, explainable artificial intelligence (XAI) techniques, including Grad-CAM and Grad-CAM++, were applied to provide clinically interpretable visual explanations by highlighting diagnostically relevant regions in mammograms. These results indicate that the proposed MobileNetV1–ViT–Bagging framework recommends an effective, computationally structured, and explainable solution for multi-class BI-RADS BC diagnosis, with strong potential for clinical decision-support applications.