Bridging modalities: a deep learning framework for brain tumor classification via CT-MRI integration and model fusion.

Overview

1. Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka, Saudi Arabia
2. College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
3. Faculty of Computing and Information, Al-Baha University, Alaqiq, Saudi Arabia
4. REGIM-Lab: Research Groups in Intelligent Machines, National School of Engineers of Sfax (ENIS), University of Sfax, Sfax, Tunisia
5. Computer Science Department, Faculty of Computers & Information Technology, University of Tabuk, Tabuk, Saudi Arabia
6. Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
7. Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah, Saudi Arabia
8. Department of Information Systems, Faculty of Computing and Information Technology, Northern Border University, Rafha, Saudi Arabia

Abstract

Artificial intelligence (AI) and machine learning (ML) have shown remarkable promise in advancing medical image analysis, yet their potential in neurology and psychiatry remains underexplored. This work explores the use of deep learning approaches for automated brain tumor classification, leveraging multimodal neuroimaging data comprising computed tomography (CT) and magnetic resonance imaging (MRI) scans. Two model families were evaluated: a custom CNN trained from scratch and a transfer-learning approach based on ResNet-18. Models were trained and validated separately on CT and MRI datasets, and further extended to a combined dataset through multimodal fusion. Experimental results demonstrate that the CNN achieved accuracies of 97 and 99% on CT and MRI datasets, respectively, outperforming ResNet18, which yielded 95 and 97% under the same settings. On the combined dataset, CNN maintained superior performance (98%) compared to ResNet18 (94%), highlighting the adaptability of CNNs to domain-specific features in medical imaging. These findings suggest that lightweight CNNs can be highly effective for neuroimaging-based tumor detection, particularly when multimodal data are leveraged. Beyond clinical utility in early diagnosis, the authors underscore the importance of exploring modality-specific characteristics and model adaptability in designing AI-driven diagnostic systems for neurological disorders.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• kaggle.com/datasets/murtozalikhon, at Kaggle; found in “Data availability statement”

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found at: https://www.kaggle.com/datasets/murtozalikhon/brain-tumor-multimodal-image-ct-and-mri.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 7 authors, 8 keywords, 30 references.

Cite

This paper

Almadhor, A., Alsubai, S., Ben Aoun, N., Al Hejaili, A., Salhi, A., Alsubait, T., & Hamad Aljahani, F. (2026). Bridging modalities: a deep learning framework for brain tumor classification via CT-MRI integration and model fusion. Frontiers in computational neuroscience, 20, 1798561. https://doi.org/10.3389/fncom.2026.1798561

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