Breast-Path-ViT: Explainable ViT for Breast Cancer Region Detection & Risk Stratification in WSIs

While manual diagnosis is frequently subjective and resource-dependent, early and accurate detection of oral cancer can greatly improve patient outcomes. In order to facilitate transparent clinical decision-making, this study offers a deep learning-based classification model that combines explainable AI techniques with a Convolutional Neural Network (CNN).The system is deployed through a lightweight web interface capable of providing real-time predictions for cancerous and non-cancerous oral images. Experimental results demonstrate strong classification performance, and the explainability layer highlights the regions contributing to the model’s predictions, enhancing interpretability for medical users. The proposed framework shows potential as an assistive tool for improving screening efficiency in diverse healthcare settings.