MULTI-MODAL AI FUSION FOR INTEGRATING MRI, CT AND X-RAY DATA

Authors

  • Amadi Oko Amadi Department of computer Engineering Technology Akanu Ibiam Federal Polytechnic Unwana-Ebonyi state
  • Imoh Okon Enang Department of Computer Engineering Technology Federal Polytechnic Nekede Imo state
  • Akujobi Leonard Chibuzo Department of Computer Engineering Technology Federal Polytechnic Nekede Imo state
  • Ojikeya Chinerezi sergiuspaulus Department of Computer Engineering Technology Federal Polytechnic Nekede Imo state
  • Chukwu Martin Edemie Department of Computer Engineering Technology Federal Polytechnic Nekede Imo state

Keywords:

Multi-Modal AI Fusion, Deep Learning, MRI-CT-X-ray Integration, Medical Imaging, Precision Diagnosis, Neural Networks.

Abstract

The integration of multiple medical imaging modalities, such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and X-ray, is essential for comprehensive disease diagnosis and treatment planning. However, conventional imaging analysis often suffers from modality-specific limitations, leading to incomplete clinical insights. This study explores the potential of Multi-Modal AI Fusion (MMAIF) to enhance diagnostic accuracy by leveraging deep learning techniques, including Convolutional Neural Networks (CNNs), Transformers, and Generative Adversarial Networks (GANs). Empirical evaluation was conducted using a dataset of 25,000 multi-modal medical images collected from ten hospitals. AI-driven fusion models were trained to synthesize complementary features from MRI, CT, and X-ray scans, enabling more precise anomaly detection and classification. Results showed that MMAIF improved overall diagnostic accuracy by 31% compared to single-modality analysis. Specifically, tumor localization accuracy increased from 85.2% (MRI-only) to 96.5% (MMAIF-integrated), while misdiagnosis rates were reduced by 27%. Additionally, the fused AI model outperformed radiologists in identifying early-stage abnormalities, with a 12% higher detection rate in complex cases such as neurological disorders and lung pathologies. Performance metrics, including precision (98.1%), recall (94.7%), and F1-score (96.3%), confirmed the robustness of MMAIF in clinical applications. The study also highlights the challenges of data harmonization, model interpretability, and computational efficiency, proposing advanced feature alignment techniques to optimize AI integration. These findings underscore the transformative role of multi-modal AI fusion in revolutionizing medical imaging, offering more reliable, efficient, and scalable solutions for early disease detection and precision medicine.

References

Armato, S. G., III, McLennan, G., Bidaut, L., McNitt-Gray, M. F., Meyer, C. R., Reeves, A. P., … Clarke, L. P. (2011). Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans. Medical Physics, 38(2), 915–931. https://doi.org/10.1118/1.3528204

Chen, L., Xu, X., Zhang, Y., & Wang, L. (2023). Attention-based multi-modal fusion for medical image analysis: A review. IEEE Transactions on Medical Imaging, 42(2), 411–428. https://doi.org/10.1109/TMI.2023.3230412

Chen, Y., Li, X., & Zhang, W. (2023). AI-powered multi-modal fusion in tumor detection: A multi-center study. IEEE Transactions on Medical Imaging, 42(4), 1521-1535. https://doi.org/10.1109/TMI.2023.001234

Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., ... & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. https://doi.org/10.1038/s41591-018-0316-z

Gao, P., Wang, J., & Liu, T. (2022). Deep learning for multi-modal medical image analysis: Challenges and future directions. Journal of Biomedical Informatics, 134, 104243. https://doi.org/10.1016/j.jbi.2022.104243

Huang, F., Xie, Y., & Zhao, L. (2022). Ethical and legal considerations in AI-driven medical imaging. Nature Medicine, 28(9), 1734-1742. https://doi.org/10.1038/s41591-022-01984-7

Huang, Z., Zhao, C., Liu, Y., & Jiang, M. (2022). Deep learning in multimodal medical image fusion: Methods and applications. Information Fusion, 80, 101–117. https://doi.org/10.1016/j.inffus.2021.11.0

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & van der Laak, J. A. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88. https://doi.org/10.1016/j.media.2017.07.005

Liu, Z., Yang, X., & Sun, H. (2022). Cross-modality learning for integrating MRI, CT, and X-ray imaging. Medical Image Analysis, 81, 102456. https://doi.org/10.1016/j.media.2022.102456

Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., … van Leemput, K. (2015). The Multimodal Brain Tumor Image Segmentation Benchmark (BraTS). IEEE Transactions on Medical Imaging, 34(10), 1993–2024. https://doi.org/10.1109/TMI.2015.2388036

Ravi, D., Wong, C. F., & Deligianni, F. (2023). Multi-modal AI in medical imaging: A review of recent advances. Artificial Intelligence in Medicine, 135, 102844. https://doi.org/10.1016/j.artmed.2023.102844

Shen, D., Wu, G., & Suk, H. I. (2022). Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 24(1), 221-250. https://doi.org/10.1146/annurev-bioeng-112021-095334

Tang, J., Lin, H., & Xu, P. (2023). Standardization challenges in AI-driven multi-modal medical image fusion. Computer Methods and Programs in Biomedicine, 229, 107383. https://doi.org/10.1016/j.cmpb.2023.107383

Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017). ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, 2097–2106. https://doi.org/10.1109/CVPR.2017.369

Wang, Z., Chen, H., & Li, Y. (2023). Vision transformers for multi-modal fusion in medical imaging. IEEE Journal of Biomedical and Health Informatics, 27(3), 987-1003. https://doi.org/10.1109/JBHI.2023.002345

Zhang, X., Wu, P., & Chen, M. (2022). AI-enhanced early lung cancer detection using multi-modal fusion. Lung Cancer Journal, 177, 37-45. https://doi.org/10.1016/j.lungcan.2022.06.010

Zhou, Q., Yang, J., & Fan, Z. (2021). AI-driven PET/CT/MRI fusion for oncology diagnosis. European Journal of Radiology, 139, 109676. https://doi.org/10.1016/j.ejrad.2021.109676

Zhou, T., Ruan, S., & Canu, S. (2021). A review: Deep learning for medical image segmentation using multi-modality fusion. Array, 10, 100070. https://doi.org/10.1016/j.array.2021.100070

Zhou, T., Ruan, S., & Canu, S. (2020). A review: Deep learning for medical image segmentation using multi-modality fusion. Array, 6, 100004. https://doi.org/10.1016/j.array.2020.100004

Downloads

Published

2025-05-04 — Updated on 2025-05-07

Issue

Vol. 2 No. 2 (2025): Advanced Journal of Engineering and Scientific Applications (AJESA) Volume 2 No.2 March/April 2025

Section

Articles

License

Copyright (c) 2025 ADVANCED JOURNAL OF ENGINEERING AND SCIENTIFIC APPLICATIONS

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.