Journal of Algorithms and Computation
Open Access

Enhancing Personalized Medicine for GBM Patients through Medical Image Analysis Using Generative Models and Deep Learning

Document Type : Research Paper

Authors

Toktam Khatibi 1
Niloufar Naddafi 2
Pooya Mazloomi 3

1 School of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran

2 Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran

3 Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran

https://doi.org/10.22059/jac.2025.393906.1227
Abstract
Glioblastoma multiforme (GBM) represents about 45.6% of primary malignant brain tumors and is marked by rapid growth and resistance to treatment, resulting in a poor prognosis for patients. This study aims to propose a personalized medicine model tailored for patients with GBM with analyzing MRI images and clinical data from 23 patients. Our research encompassed three primary scenarios. In Scenario 1, we constructed a hybrid model combining VIT and Auto-Encoder approaches applied to patient MRI data, achieving an impressive accuracy rate of 96% in determining optimal treatment dosages. For Scenario 2, we introduced Gaussian noise to the MRI images, reflecting real-world conditions, resulting in a drop in model accuracy to 72%. In Scenario 3, we restored the noisy images using advanced techniques, which led to an improved accuracy of 94%. It demonstrates that our proposed scenarios can effectively identify optimal radiotherapy dosages for GBM patients.

Keywords

glioblastoma multiforme
radiotherapy
magnetic resonance imaging
deep learning
image noise reduction
Journal of Algorithms and Computation
Volume 57, Issue 1
August 2025
Pages 41-58

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