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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics
Abdelmadjid Benmachiche
Laboratory of Computer Science and Applied Mathematics, Dept. of Computer Science, Faculty of Science and Technology, Chadli Bendjedid, University, El-Tarf, Algeria.
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Intelligent Fusion of Multi-Modal Medical Imaging: A Comprehensive Review of Methods, Challenges, and Clinical Integration Majda Maatallah; Abdelmadjid Benmachiche; Khadija Rais; Salma Touam
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1527

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.
Comparative Analysis of Attention Mechanisms in Pix2Pix for Multimodal MRI Fusion Ali-Abdelatif Betouil; Abdelmadjid Benmachiche; Khadija Rais; Amel Sahki; Imene Soualmia
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1720

Abstract

Medical image fusion (MIF) is a key technique in medical imaging, which combines complementary information from different imaging modalities, thereby improving the accuracy of diagnosis, particularly for lesion detection and treatment planning. Deep learning has significantly advanced this area, with the development of generative models and transformers leading to improvements in fidelity and accuracy, although the study of the influence of attention mechanisms on these models remains limited to a single type or a single architectural placement. This paper offers an analytical examination of the architectures of Pix2Pix with three attention mechanisms (spatial attention, channel attention (Squeeze-and-Excitation), and self-attention), where they are tested in three different placement strategies (encoder-only, decoder-only, and encoder-decoder), using the BraTS2020 dataset, with training supervised by a pseudo-ground-truth derived from arithmetic averaging. We fused six MRI modality pairs (FLAIR-T1, FLAIR-T1ce, FLAIR-T2, T1-T1ce, T1-T2, T1ce-T2), evaluating them using different metrics, including SSIM, PSNR, NMI, Entropy, and QAB/F. Results show that, in all cases, attention integration can significantly improve the quality of fusion over baseline methods, including cGAN and standard Pix2Pix. Spatial attention with encoder-decoder placement shows the best results, with SSIM values up to 0.91 and PSNR superior to 25 dB for the heterogeneous modality pair FLAIR-T1. Similarly, channel and self-attention demonstrate their effectiveness, especially with encoder-decoder placements. Based on these findings, attention-based fusion systems can be practically designed in a way that enhances MMIF, and the importance of designing attention in accordance with the nature of the modality is emphasized for optimal fusion performance. Our study demonstrates its effectiveness and may serve as a foundation for future research.
Co-Authors Ali-Abdelatif Betouil Amel Sahki Imene Soualmia Khadija Rais Khadija Rais Majda Maatallah Salma Touam