Deep-lymph: An advanced deep learning framework for precision diagnosis of lymphoma from histopathological images

Deep learning models have shown great promise in medical image classification, but their lack of interpretability limits their adoption in clinical settings. This study addresses the need for explainable models in lymphoma diagnosis using an enhanced model within a transfer learning framework. To improve interpretability, we incorporated Explainable Artificial Intelligence (XAI) techniques, including SHAP, LIME, Grad-CAM, and Grad-CAM++, Occlusion Sensitivity Map to provide insights into the model's decision-making process. The model was trained on high-quality lymphoma imaging data, and preprocessing techniques such as Denoising, Contrast Limited Adaptive Histogram Equalization (CLAHE), and Gamma Correction were applied to improve image clarity. We conducted an ablation study to identify optimal parameters for the model. Our proposed model achieved an accuracy of 99.99%, with precision and recall rates of 100%, demonstrating its exceptional performance. SHAP and LIME helped in understanding the model's decisions, while Grad-CAM and Grad-CAM++ identified the crucial image features that influenced classification, enhancing transparency and trust in AI-assisted lymphoma diagnosis. This study contributes to advancing the use of deep learning in oncology, offering a reliable and interpretable tool for lymphoma detection. • Developed an explainable AI framework for accurate lymphoma image classification. • Integrated SHAP, LIME, Grad-CAM, Grad-CAM++, and Occlusion Sensitivity for interpretability. • Applied preprocessing techniques including denoising, CLAHE, and gamma correction. • Conducted ablation study to determine optimal parameters of the proposed model. • Achieved 99.99% accuracy with 100% precision and recall in lymphoma detection.