Integrating Deep Learning and Traditional Learning for Content Based Image Retrieval System in Lymphoma Diagnosis

Lymphoma diagnosis relies on accurate and timely identification of lymphoma patterns, and Content-Based Image Retrieval (CBIR) systems leveraging medical imaging have shown promise in this regard. This study proposes a novel framework that integrates deep learning and traditional learning methodologies to enhance CBIR systems for lymphoma diagnosis. The approach uses convolutional neural networks (CNNs), a type of neural network that is deep, to derive a high degree information from photos of lymphoma.