Hybrid CNN-GCN framework for brain tumor MRI classification: A graph-based approach to smart healthcare diagnostics.

Overview

1. Department of Allied Medical Sciences, Faculty of Applied Medical Sciences, Al al‐Bayt University, Mafraq, Jordan
2. Department of Physics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
3. Department of Laser and Optoelectronics Engineering, Dijlah University, Baghdad, Iraq
4. School of Physics, Universiti Sains Malaysia, Penang, Malaysia
5. Computer Science Department Faculty of Information Technology, Applied Science Private University, Amman, Jordan
6. Deanship of Scientific Research, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
7. Department of Mathematics, Faculty of Science, Ajloun National University, Ajloun, Jordan

Abstract

Background: Accurate classification of brain tumors is a major challenge in neuro‐oncology, as the heterogeneity of tumor morphology and the overlap of radiological features limit the effectiveness of conventional diagnostic approaches. Early and reliable tumor characterization is essential for treatment planning, prognosis, and improved patient outcomes. Recent advances in artificial intelligence (AI) have enabled the development of deep learning frameworks that can augment radiological interpretation and support clinical decision‐making.

Objective: This study proposes and validates a hybrid computational framework that integrates convolutional neural networks (CNNs) with graph convolutional networks (GCNs) for automated classification of brain tumors from magnetic resonance imaging (MRI).

Methods: A publicly available Kaggle‐based MRI dataset was utilized, consisting of four categories: glioma, meningioma, pituitary tumor, and no‐tumor. The proposed pipeline incorporated systematic preprocessing, transfer learning via the InceptionV3 architecture for hierarchical feature extraction, graph construction to model inter‐feature relationships, and GCN‐based relational learning for final classification. Hyperparameter optimization was performed using Particle Swarm Optimization (PSO) to improve generalizability.

Results: The experimental evaluation achieved an overall classification accuracy of 92.91%. Class‐specific performance analysis demonstrated particularly high diagnostic accuracy in the no‐tumor group (F1‐score: 0.9963) and pituitary tumor group (F1‐score: 0.9599). The incorporation of PSO tuning further improved the validation accuracy to 94.23%. The hybrid CNN–GCN framework exhibited robustness against imaging artifacts and irregular tumor boundaries, conditions that commonly challenge conventional classification techniques.

Conclusion: The integration of CNN‐based hierarchical feature extraction with GCN‐based relational reasoning provides a significant advancement in automated brain tumor classification. This reproducible and intelligent diagnostic pipeline demonstrates strong potential for clinical translation by enhancing diagnostic precision, reducing radiologist workload, and facilitating timely therapeutic interventions. The findings support the integration of graph‐based deep learning systems into smart healthcare ecosystems, where AI‐assisted diagnostic tools can contribute to improved outcomes in neuro‐oncology.

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

Code

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Data

Datasets cited

• kaggle.com/datasets/masoudnickparvar — at Kaggle; found in “DATA AND CODE AVAILABILITY”

Data and code availability

The brain tumor MRI dataset used in this study is publicly available on Kaggle at https://www.kaggle.com/datasets/masoudnickparvar/brain‐tumor‐mri‐dataset (https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset). The source code for the proposed CNN‐GCN framework, including preprocessing scripts, model architecture implementations, and training procedures, is available at https://www.kaggle.com/code/araraltawil/brain‐tumor‐mri‐analysis‐with‐cnn‐gnn‐pipeline (https://www.kaggle.com/code/araraltawil/brain-tumor-mri-analysis-with-cnn-gnn-pipeline) or upon reasonable request to the corresponding author. The implementation requires Python 3.9+, PyTorch 2.0+, PyTorch Geometric 2.3+, and TensorFlow/Keras 2.12+ for reproducibility.

ETHICS STATEMENT: Ethical approval was obtained from Universiti Sains Malaysia, School of Physics Ethics Committee.

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

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 6 keywords, 9 MeSH terms, 1 funder, 40 references, 1 integrity notice.

Cite

This paper

Alkasasbeh, M. S., Ibnaouf, K. H., Ahmed, N. M., Rahman, A. A., Abbas, D. N., Al Tawil, A., Idriss, H., & Alkasasbeh, H. T. (2026). Hybrid CNN-GCN framework for brain tumor MRI classification: A graph-based approach to smart healthcare diagnostics. Journal of applied clinical medical physics, 27(4), e70560. https://doi.org/10.1002/acm2.70560

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