Integrating Deep Learning and Traditional Learning for Content Based Image Retrieval System in Lymphoma Diagnosis
Year: 2025
Type: article
Abstract: 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.
Source: Sylwan
DOI: 10.59879/p1k2s
Authors: R. Inbaraj, R.B. Dravidapriyaa, Naveenya B P, Mathivanan Durai
Institutions: Bharathidasan University, Chitkara University, Hongik University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409476970