A Graph Based Attention Model and Calibrated Random Forest for Breast Cancer Classification using Histopathology Images

Abstract

Artificial intelligence and computer vision advancements have revolutionized computer-aided diagnosis (CAD) systems, enabling more accurate breast cancer (Br-Can) detection using histopathology images. This study proposes a classification framework that integrates Vision Transformers (ViT), Graph Attention Networks (GAT), and Calibrated Random Forest (CRF) to enhance diagnostic accuracy. ViT effectively captures rich visual representations and helps to form a graph-like structure, while GAT models the structural relationships within histopathology images, providing a more comprehensive understanding of tissue morphology. Extensive experiments were conducted with various model combinations, demonstrating that the ViT + GAT + CRF architecture achieved the highest performance. The experiment is carried out on the BreakHis dataset, and the model acquires an accuracy of 97.33%. These results highlight the effectiveness of incorporating both visual and structural features to improve diagnostic reliability. Our proposed framework represents a significant advancement in digital histopathology-based (BrCan) diagnosis and holds promise for broader applications in medical imaging.