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Khatibi, Toktam, Naddafi, Niloufar, Mazloomi, Pooya. (1404). Enhancing Personalized Medicine for GBM Patients through Medical Image Analysis Using Generative Models and Deep Learning. , 57(1), 41-58. doi: 10.22059/jac.2025.393906.1227
Toktam Khatibi; Niloufar Naddafi; Pooya Mazloomi. "Enhancing Personalized Medicine for GBM Patients through Medical Image Analysis Using Generative Models and Deep Learning". , 57, 1, 1404, 41-58. doi: 10.22059/jac.2025.393906.1227
Khatibi, Toktam, Naddafi, Niloufar, Mazloomi, Pooya. (1404). 'Enhancing Personalized Medicine for GBM Patients through Medical Image Analysis Using Generative Models and Deep Learning', , 57(1), pp. 41-58. doi: 10.22059/jac.2025.393906.1227
Khatibi, Toktam, Naddafi, Niloufar, Mazloomi, Pooya. Enhancing Personalized Medicine for GBM Patients through Medical Image Analysis Using Generative Models and Deep Learning. , 1404; 57(1): 41-58. doi: 10.22059/jac.2025.393906.1227

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

Journal of Algorithms and Computation
مقاله 4، دوره 57، شماره 1، آبان 2025، صفحه 41-58    اصل مقاله (1.17 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jac.2025.393906.1227
نویسندگان
Toktam Khatibi* 1؛ Niloufar Naddafi2؛ Pooya Mazloomi3
1School of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
2Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
3Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
چکیده
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.
کلیدواژه‌ها
glioblastoma multiforme؛ radiotherapy؛ magnetic resonance imaging؛ deep learning؛ image noise reduction
آمار
تعداد مشاهده مقاله: 130
تعداد دریافت فایل اصل مقاله: 62





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