Intelligent Application and Model for Enhancing Breast Cancer Classification

Abstract

Breast cancer diagnosis using mammography remains a challenging task, particularly in the presence of dense and heterogeneous tissue structures. While deep learning (DL) methods have shown promise, their interpretability and performance are often constrained by suboptimal configurations and limited contextual learning. This paper introduces a Vision Transformer (ViT)-based model, enhanced by the Reptile Search Algorithm (RSA), to achieve both accurate classification and explainability. The ViT framework captures global dependencies in mammographic image patches, while RSA optimizes critical hyperparameters, including patch size, learning rate, and attention depth, to enhance model convergence and generalization. Applied on the CBIS-DDSM: Breast Cancer Image Dataset, the ViT-RSA model attains a classification accuracy of 96.2% and an Area Under the Curve (AUC) of 0.97. Interpretability is ensured through attention visualization maps from ViT layers and Grad-Cam-based explanations. The experimental results confirm that ViT-RSA outperforms conventional CNN models, offering a robust and interpretable tool for breast cancer diagnosis that supports clinical decision-making.