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February 21, 2026Intelligence-Based MedicineOpen Access

Deep-Lymph: An Advanced Deep Learning Framework for Precision Diagnosis of Lymphoma from Histopathological Images

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Authors

MHMd. Jakir HossenEMEram MahamudMAMd. Assaduzzaman

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Overview

Advanced deep learning achieves 99.99% accuracy in lymphoma diagnosis, enhancing interpretation in clinical settings.

Key Points

  • To develop an explainable deep learning model for precise lymphoma diagnosis from histopathological images.
  • Incorporated XAI techniques including SHAP, LIME, Grad-CAM, Grad-CAM++, and Occlusion Sensitivity Map for model interpretability.
  • Applied preprocessing techniques like denoising, CLAHE, and gamma correction to improve image clarity.
  • Conducted an ablation study to identify optimal parameters for the model.
  • Achieved 99.99% accuracy with 100% precision and recall rates in lymphoma detection.
  • Utilized XAI methods to provide insights into model decision-making, enhancing transparency.

Cite This Study

Hossen et al. (2026) studied this question.

synapsesocial.com/papers/69994ba9873532290d01fd95https://doi.org/10.1016/j.ibmed.2026.100347
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