Intelligent Fusion of Multi-Modal Medical Imaging: A Comprehensive Review of Methods, Challenges, and Clinical Integration

Majda Maatallah; Abdelmadjid Benmachiche; Khadija Rais; Salma Touam

doi:10.35882/jeeemi.v8i3.1527

Majda Maatallah Laboratory of Computer Science and Applied Mathematics, Dept. of Computer Science, Faculty of Science and Technology, Chadli Bendjedid, University, El-Tarf, Algeria https://orcid.org/0000-0002-3445-2940
Abdelmadjid Benmachiche Laboratory of Computer Science and Applied Mathematics, Dept. of Computer Science, Faculty of Science and Technology, Chadli Bendjedid, University, El-Tarf, Algeria. https://orcid.org/0000-0002-0690-2625
Khadija Rais Laboratory of Mathematics, Informatics and Systems (LAMIS), Echahid Cheikh Larbi Tebessi University, Tebessa, Algeria https://orcid.org/0009-0004-3907-7782
Salma Touam Laboratory of Physical-Chemistry of Materials, Dept. of Physics, Faculty of Science and Technology, Chadli Bendjedid University, El Tarf, Algeria. https://orcid.org/0000-0002-2513-7703

DOI: https://doi.org/10.35882/jeeemi.v8i3.1527

Keywords: Multimodal Medical Image Fusion (MMIF), Deep Learning, Feature-Level Fusion, Pixel-Level Fusion, Decision-Level Fusion, Medical Imaging

Abstract

Multimodal Medical Imaging Fusion (MMIF) is defined as the incorporation of information from multiple imaging modalities in a way that is mutually supplementary, thereby addressing limitations associated with using a single imaging modality to evaluate a patient and increasing diagnostic accuracy. Further, this review provides a dedicated synthesis of deep learning architectures in MMIF, examining CNN-based hybrids, attention-enhanced transformers, GAN-driven unsupervised fusion, and emerging diffusion models. The state of the art in MMIF can be classified into three levels of fusion: (1) pixel level, fusion of raw pixel intensity values to preserve spatial detail; (2) feature level, features are derived from textures, edges, and region-of-interest (ROI) descriptors; (3) decision level, fusing independent outputs of each source using ensemble or rule-based methods to produce a single, integrated output from all sources, potentially improving interpretability of the integrated output. The use of AI algorithms improves fusion outcomes by yielding higher-quality results. However, clinicians' confidence in deep-learning-based models is limited due to their inability to generalise across multiple scanners, protocols, and medical systems. This analysis demonstrates that clinical AI systems must be developed with interpretability as a core attribute, to provide an explanation of how each modality is contributing to the final decision, and to establish a fusion policy that preserves the ability to make accurate diagnostic determinations based on fused images. In addition to developing more sophisticated algorithms, future developments in MMIF will require collaborative partnerships between developers and clinicians to develop fused images into reliable diagnostic tools to be used in precision medicine.

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