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International Journal of Computational and Electronics Aspects in Engineering (IJCEAE)
Volume 7 · Issue 4 · September 2026 · pp. 416–436
Review Article · Peer Reviewed
Received: July 16, 2026 · Accepted: September 02, 2026 · Published: September 14, 2026
DOI: 10.26706/ijceae.7.4.20260809
Open Access · CC BY 4.0

Improving Brain MRI for Early Tumor Diagnosis Using Deep Learning Generative Adversarial Networks: A Comprehensive Review

Omaima Nazar A. AlAllaf AlMustawi1,*, Shahla AbdulWahhab AbdulQader2
1Department of Information Systems, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia.
2Department of Network Technologies and Intelligent Systems Engineering, Mosul Polytechnic College, Northern Technical University, Mosul, Iraq.

*Corresponding author: omaimaalallaf@gmail.com
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Abstract

Brain Tumor is an abnormal growth of cells in the brain that grows rapidly, invades surrounding tissue, and is often life-threatening. Therefore, there is a need to improve Magnetic Resonance Imaging (MRI) for early tumor diagnosis using Generative adversarial networks (GAN). A comprehensive review is conducted in this study to discuss the related literature that improve MRI for early Tumor Diagnosis using recently developed GAN. We analyzed several methods that were conducted by these literature studies and created a brief classification.

The key challenges associated with achieving high-accuracy Tumor Diagnosis were addressed also. The literature references had been identified from 4 good reputable Databases: IEEE XPLORE, Web of Science, Scopus and PubMed. Publications were taken in recent years, specifically 2020-2024, because the GAN technology was developed in 2014 and it was largely accelerated. We excluded the researches with irrelevant titles and also the papers on other Tumors in human body. This study was conducted between January and May 2025. Then, we summarized the results of the collected articles. We found 45 related articles, about 25 of them presented actual attempts to synthesize images and provide accurate segmentation/classification using single/multiple landmarks. The other 20 articles discuss the recent advances in GANs, do practical experiments, and contain analytical studies of Brain Tumor. As a result, set of recommendations were provided to improve Deep Learning GAN approaches to enhance MRI for early Brain Tumor diagnosis systems.

Keywords

Magnetic Resonance Imaging (MRI) Machine Learning (ML) Deep Learning (DL) Generative Adversarial Networks (GAN) Convolutional Neural Network (CNN)

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