Enhancing lymphoma cancer detection using deep transfer learning on histopathological images.

Lymphoma histopathological diagnosis is complex due to rare subtypes, morphological overlaps, and poor tumor differentiation. In this paper, an AI-based system using deep transfer learning and simulated federated learning is developed to classify two lymphoma types i.e. Chronic Lymphocytic Leukemia (CLL) and Follicular Lymphoma (FL) from a dataset of 4500 histopathological images. Six models (VGG-16, VGG-19, MobileNetV2, ResNet50, DenseNet161, and Inception V3) were evaluated across four data thresholds (0.05 to 0.2). These models used fine-tuned convolutional layers to automatically extract high-level image features relevant to tissue morphology; the extracted features were processed internally through each model's classifier, forming an end-to-end classification pipeline. DenseNet161 achieved the best classification performance across thresholds, while Inception V3 showed the highest accuracy (97.5%) and lowest RMSE (0.393) in the testing phase using deep learning. A simulated federated learning setup was also explored, where Inception V3 again outperformed other models, indicating its robustness in decentralized learning scenarios. The reported evaluation metrics loss, accuracy, precision, RMSE, F1 score, and recall, are derived from the testing phase, ensuring an accurate assessment of generalization performance. The findings highlight the efficacy of deep transfer learning in early and accurate lymphoma detection, with Inception V3 and DenseNet161 demonstrating strong performance across both learning paradigms. However, since federated learning was not fully deployed in a real-world distributed environment, its broader applicability remains a subject for future exploration.