A deep learning based NeuroFusionNet approach for automated brain tumor diagnosis from MRI.

Overview

1. Department of Radiology, Korean Medical Center, Lusail, Qatar
2. Computer Science Department, Faculty of Information Technology and Computer Sciences, Yarmouk University, Irbid, Jordan
3. Department of Medical Imaging, Faculty of Allied Medical Sciences, Isra University, Amman, Jordan
4. Department of Allied Medical Sciences-Radiologic Technology, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid, Jordan
5. Department of Information Systems, Faculty of Information Technology and Computer Science, Yarmouk University, Irbid, Jordan
6. Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan
7. Department of Medical Imaging, Faculty of Applied Medical Sciences, The Hashemite University, Zarqa, Jordan
8. Physics Department, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia

Abstract

Background: Brain tumor diagnosis from magnetic resonance imaging (MRI) remains a challenging task due to the high variability in tumor appearance and the limitations of manual interpretation.

Methods: To address these challenges, this paper proposes NeuroFusionNet, a deep learning framework for automated brain tumor classification from MRI. The framework integrates GAN-based synthetic image generation with transfer learning using a fine-tuned VGG16 backbone. Real and GAN-generated MRI images are passed through VGG16 to extract discriminative feature representations, which are then used for final classification. To adapt the model to domain-specific MRI characteristics while preserving pretrained knowledge, the last ten layers of VGG16 are fine-tuned and the remaining layers are kept frozen.

Results: The effectiveness of NeuroFusionNet is validated on two publicly available brain MRI datasets. Experimental results demonstrate that the proposed learning framework achieves classification accuracies of 99.05 and 98.75% on the Brain Tumor MRI Dataset and the MRI with Bounding Boxes Dataset, respectively, consistently outperforming several state-of-the-art neural architectures, including VGG16, VGG19, MobileNetV2, DenseNet121, and NASNetLarge.

Conclusion: The results suggest that NeuroFusionNet is effective for the evaluated public MRI datasets; additional external validation is required.

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

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Data

Datasets cited

• kaggle.com/datasets/ahmedsorour1, at Kaggle; found in the references
• kaggle.com/datasets/deeppythonist, at Kaggle; found in the references

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

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, 12 authors, 6 keywords, 1 funder, 25 references.

Cite

This paper

Mustafa, O., Alhatamleh, S., Mhanna, H. Y. A., Almahmoud, A., Malkawi, R., Malkawi, M., Yaseen, A.-B. B., Akhdar, H. F., Malkawi, H., Maashey, F., Alghulayqah, L., & Amin, M. (2026). A deep learning based NeuroFusionNet approach for automated brain tumor diagnosis from MRI. Frontiers in neuroinformatics, 20, 1795354. https://doi.org/10.3389/fninf.2026.1795354

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