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Dergiler / Süleyman Demirel Üniversitesi Fen Edebiyat Fakültesi Fen Dergisi / 2025 / Volume: 20 Issue: 2

Hybrid Deep Learning and Reinforcement Learning Approach for Brain Tumor Classification from MRI Images

Çiğdem Saraç(AKDENIZ UNIVERSITY)0000-0002-3538-6551
Seda Arıkan Arıbal(AKDENIZ UNIVERSITY)0009-0009-9067-0502
Yiğit Ali Üncü(AKDENIZ UNIVERSITY)0000-0001-7398-9540
Sayfa
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Yayın Tarihi
2025-11-26
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Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

This study demonstrates that combining deep learning with reinforcement learning enhances model adaptability, offering a more reliable and effective diagnostic approach.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Brain cancer, resulting from abnormal tumor growth in brain tissue, requires accurate and timely diagnosis.
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Temel Bulgular & Veri:To address this, we propose a hybrid system combining deep learning (VGG16, ResNet50, DenseNet201) with reinforcement learning (Q-learning) for brain tumor classification.
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Sonuç & Katkı:This study demonstrates that combining deep learning with reinforcement learning enhances model adaptability, offering a more reliable and effective diagnostic approach.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Brain tumor · MRI classification · Deep learning

Abstract

Brain cancer, resulting from abnormal tumor growth in brain tissue, requires accurate and timely diagnosis. Although MRI plays a crucial role, manual interpretation is prone to errors and delays. To address this, we propose a hybrid system combining deep learning (VGG16, ResNet50, DenseNet201) with reinforcement learning (Q-learning) for brain tumor classification. Using three distinct MRI datasets within MATLAB, the models achieved high classification accuracies: 97.45% (VGG16), 96.06% (ResNet50), and 96.93% (DenseNet201). The integration of reinforcement learning improved decision-making and interpretability. Additionally, a user-friendly interface was developed to support clinical decision-making. This study demonstrates that combining deep learning with reinforcement learning enhances model adaptability, offering a more reliable and effective diagnostic approach.

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Bu Makaleye Atıf Yap

Saraç, Ç., Arıbal, S. A., Üncü, Y. A. (2025). Hybrid Deep Learning and Reinforcement Learning Approach for Brain Tumor Classification from MRI Images. Süleyman Demirel Üniversitesi Fen Edebiyat Fakültesi Fen Dergisi, Volume: 20 Issue: 2. https://doi.org/10.29233/sdufeffd.1694369

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