Survey on Multi-Source Medical Imaging Fusion for Classification and Retrieval: Current Status and Available Datasets

• Published in: SN computer science Vol. 6; no. 7; p. 773
• Main Authors: Abderrahim, Marwa, Baâzaoui, Abir, Barhoumi, Walid
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 26.08.2025; Springer Nature B.V
• Subjects: CAD; Classification; Computer aided design; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Computer vision; Data Structures and Information Theory; Datasets; Decision making; Image retrieval; Information Systems and Communication Service; Magnetic resonance imaging; Mammography; Medical imaging; Nodules; Pattern Recognition and Graphics; Retrieval; Software Engineering/Programming and Operating Systems; Survey Article; Vision; Datasets; Medical imaging; Content-based image retrieval; Multi-view; Multi-modality; Classification
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-025-04299-1

Constant advances and innovation in technology, and particularly in medical imaging modalities, have enhanced diagnostic capabilities, enabling medical experts and professionals to visualize and diagnose a variety of medical conditions in order to provide better patient care. Recently, to exploit these advantages, multi-source medical image fusion has been widely applied in two highly recommended situations, namely classification-based computer-aided diagnosis (CAD) and retrieval-based CAD. In fact, the concept of multi-source medical imaging fusion has been used successfully, particularly in the context of breast cancer, cardiac magnetic resonance imaging and pulmonary nodules. In this study, we present a comprehensive survey of the latest trends in multi-source medical imaging methods in the context of classification (normal vs. abnormal) and content-based retrieval. In addition, we describe the most relevant multi-source medical image datasets that are publicly available.