MobileDANet integrating transfer learning and dynamic attention for classifying multi target histopathology images with explainable AI.

Cancer is a life-threatening disease that affects several human lives all over the world. The classification of cancer severities utilizing histopathological images is vital for effective and timely diagnosis. This always creates a demandable requirement for promising and automated computer-aided diagnosis (CAD) frameworks in clinical analysis. In this direction, the study introduces a deep learning (DL) framework aimed at classifying renal cell carcinoma (RCC) into five distinct grades, and the work is also extended to include histopathology images of breast and colon cancer. The proposed architecture, MobileDANet, integrates a MobileNetV2 backbone with a dynamic attention (DA) block (multi-head attention + MLP) to capture both long and long-range dependencies efficiently and employs Grad-CAM for interpretability. On RCC (KMC dataset) data, MobileDANet attains 90.71% accuracy (F1 score as 90.94%); on BreakHis, it achieves a recognition rate of 88.16%; and on CRCH, it reaches a weighted F1-score of 99.08%, outperforming recent baselines. In future work, the framework will be extended to larger multi-institutional datasets, pursue model compression with automated hyperparameter optimization, and explore integration with clinical-decision support systems.