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Journals / Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi / 2026 / Cilt: 42 Sayı: 2

Synthetic MRI as an Alternative Training Resource for Brain Tumor Classification: A GAN-Based Study

Beyin Tümörü Sınıflandırması için Alternatif Bir Eğitim Kaynağı Olarak Sentetik MRI: GAN Tabanlı Bir Çalışma

Ali Sakmak(KAHRAMANMARAŞ SÜTÇÜ İMAM ÜNİVERSİTESİ, FEN BİLİMLERİ ENSTİTÜSÜ)0000-0001-7143-1703
Yavuz Canbay(Kahramanmaras Sutcu Imam University, Faculty Of Engineering And Architecture, Department Of Computer Engineering)0000-0003-2316-7893
Pages
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Publication Date
2026-07-20
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These results demonstrate that GAN-generated synthetic images can provide meaningful data diversity and may serve as an alternative training resource in medical image classification tasks, particularly in scenarios where access to real data is limited.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Accurate classification of brain tumors from magnetic resonance (MR) imaging data very important for clinical decision support systems.
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Key Findings & Data:Moreover, synthetic data generation is considered not only as a mechanism for performance enhancement but also as a potential approach for supporting privacy-sensitive medical imaging applications where access to real patient data may be restricted.
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Conclusion & Impact:These results demonstrate that GAN-generated synthetic images can provide meaningful data diversity and may serve as an alternative training resource in medical image classification tasks, particularly in scenarios where access to real data is limited.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Tümör tespiti · Transfer Öğrenme · Veri Mahremiyeti

Abstract

Accurate classification of brain tumors from magnetic resonance (MR) imaging data very important for clinical decision support systems. However, the privacy issues encountered in MR data and the high cost of data collection processes make it difficult to develop artificial intelligence-based detection systems. Hence, synthetic data generation has emerged as a promising solution to mitigate data scarcity while preserving patient privacy. This study evaluates the feasibility of using synthetic MRI data as an alternative training resource and compares real, synthetic, and combined data for training scenarios under a unified experimental framework. Moreover, synthetic data generation is considered not only as a mechanism for performance enhancement but also as a potential approach for supporting privacy-sensitive medical imaging applications where access to real patient data may be restricted. In this paper, three experimental scenarios were considered: real MR images only, GAN-generated synthetic MR images only, and a hybrid dataset combining both. Transfer learning models were used and their classification success was evaluated using various metrics. The findings show that models perform better with synthetic images alone, while in some models, using original and synthetic data together can have a positive impact on performance. These results demonstrate that GAN-generated synthetic images can provide meaningful data diversity and may serve as an alternative training resource in medical image classification tasks, particularly in scenarios where access to real data is limited.

Özet

Manyetik rezonans (MR) görüntüleme verilerinden beyin tümörlerinin doğru sınıflandırılması, klinik karar destek sistemleri için büyük önem taşımaktadır. Bununla birlikte, MR verilerinde karşılaşılan mahremiyet sorunları ve veri toplama süreçlerinin yüksek maliyeti, yapay zekâ tabanlı tespit sistemlerinin geliştirilmesini zorlaştırmaktadır. Bu bağlamda, sentetik veri üretimi, hasta mahremiyetini korurken veri kıtlığını hafifletmek için umut vadeden bir çözüm olarak ortaya çıkmıştır. Bu çalışma, alternatif bir eğitim kaynağı olarak sentetik MR verilerinin kullanılabilirliğini değerlendirmekte ve gerçek, sentetik ve birleştirilmiş eğitim senaryolarını birleşik bir deneysel çerçeve altında karşılaştırmaktadır. Ayrıca, sentetik veri üretimi yalnızca bir performans artırma mekanizması olarak değil, aynı zamanda gerçek hasta verilerine erişimin kısıtlanabileceği mahremiyet açısından hassas tıbbi görüntüleme uygulamalarını desteklemek için potansiyel bir yaklaşım olarak da ele alınmaktadır. Bu makalede üç deneysel senaryo ele alınmıştır: yalnızca gerçek MR görüntüleri, yalnızca GAN tarafından üretilen sentetik MR görüntüleri ve her ikisini birleştiren hibrit bir veri kümesi. Transfer öğrenme modelleri eğitilmiş ve sınıflandırma başarıları çeşitli ölçütler kullanılarak değerlendirilmiştir. Bulgular, modellerin yalnızca sentetik görüntülerle daha iyi performans gösterdiğini, bazı modellerde ise orijinal ve sentetik verilerin birlikte kullanılmasının performansı olumlu etkileyebileceğini göstermektedir. Bu sonuçlar, GAN tarafından üretilen sentetik görüntülerin anlamlı veri çeşitliliği sağlayabileceğini ve özellikle gerçek verilere erişimin sınırlı olduğu senaryolarda tıbbi görüntü sınıflandırma görevlerinde alternatif bir eğitim kaynağı olarak kullanılabileceğini göstermektedir.

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Cite This Article

Sakmak, A., Canbay, Y. (2026). Synthetic MRI as an Alternative Training Resource for Brain Tumor Classification: A GAN-Based Study. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, Cilt: 42 Sayı: 2. https://doi.org/10.65520/erciyesfen.1934731

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