Malignant lymphoma, which impacts the lymphatic system, presents diverse challenges
in accurate diagnosis due to its varied subtypes—chronic lymphocytic leukemia (CLL), follicular
lymphoma (FL), and mantle cell lymphoma (MCL). Lymphoma is a form of cancer that begins in the
lymphatic system, impacting lymphocytes, which are a specific type of white blood cell. This research
addresses these challenges by proposing ensemble and non-ensemble transfer learning models
employing pre-trained weights from VGG16, VGG19, DenseNet201, InceptionV3, and Xception.
For the ensemble technique, this paper adopts a stack-based ensemble approach. It is a two-level
classification approach and best suited for accuracy improvement. Testing on a multiclass dataset
of CLL, FL, and MCL reveals exceptional diagnostic accuracy, with DenseNet201, InceptionV3, and
Xception exceeding 90% accuracy. The proposed ensemble model, leveraging InceptionV3 and
Xception, achieves an outstanding 99% accuracy over 300 epochs, surpassing previous prediction
methods. This study demonstrates the feasibility and efficiency of the proposed approach, showcasing
its potential in real-world medical applications for precise lymphoma diagnosis.