DeepHistoViT: An Interpretable Vision Transformer Framework for Histopathological Cancer Classification

Histopathology remains the gold standard for definitive cancer diagnosis due to its ability to provide detailed cellular-level assessment of tissue morphology. However, manual histopathological examination is time-consuming, labour-intensive, and subject to inter-observer variability, creating a growing demand for reliable computer-assisted diagnostic tools. Recent advances in deep learning, particularly transformer-based architectures, have demonstrated strong potential for modelling complex spatial dependencies in medical images. In this work, we propose DeepHistoViT, a transformer-based deep learning framework for automated classification of histopathological images. The proposed model employs a customized Vision Transformer (ViT) architecture with an integrated attention mechanism designed to capture fine-grained cellular structures while enhancing interpretability through attention-based localization of diagnostically relevant regions. The framework is evaluated on three publicly available and diverse histopathology datasets covering lung cancer, colon cancer, and acute lymphoblastic leukaemia. Experimental results demonstrate that DeepHistoViT achieves state-of-the-art performance across all datasets, with classification accuracy, precision, recall, F1 score, and ROC–AUC reaching 100\% on the lung and colon cancer datasets, and 99.85\%, 99.84\%, 99.86\%, 99.85\%, and 99.99\%, respectively, on the acute lymphoblastic leukaemia dataset. All performance metrics are reported with 95\% confidence intervals to ensure statistical robustness. These findings demonstrate the effectiveness of transformer-based architectures for histopathological image analysis and highlight the potential of DeepHistoViT as a reliable and interpretable computer-assisted diagnostic tool to support pathologists in clinical decision-making. The proposed framework may contribute to improved diagnostic efficiency, consistency, and early cancer detection.​