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Journals / Turkish Journal of Science and Technology / 2026 / Volume: 21 Issue: 2

Hybrid Deep Feature Extraction and mRMR-Based Optimization-Assisted Machine Learning Approaches in MRI-Based Brain Tumor Classification

MRI Tabanlı Beyin Tümörü Sınıflandırmasında Hibrit Derin Öznitelik Çıkarımı ve mRMR Tabanlı Optimizasyon Destekli Makine Öğrenmesi Yaklaşımları

Vahdet Cemil Altun(KAHRAMANMARAS SUTCU IMAM UNIVERSITY)0000-0001-7792-0380
Yavuz Canbay(Kahramanmaraş Sütçü İmam Üniversitesi, Mühendislik ve Mimarlık Fakültesi, Bilgisayar Mühendisliği Bölümü)0000-0003-2316-7893
Pages
—
Publication Date
2026-09-30
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AI Summary & Key Takeaways

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

The SVM classifier exhibited greater performance relative to other approaches, attaining the best accuracy of 92.75% and F1-Score of 92.73% on the test set.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Brain tumors are among the important neurological diseases that require early and accurate diagnosis due to their high mortality rates and serious clinical outcomes.
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Key Findings & Data:This increases the need for computer-aided decision systems.
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Conclusion & Impact:The SVM classifier exhibited greater performance relative to other approaches, attaining the best accuracy of 92.75% and F1-Score of 92.73% on the test set.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Brain tumor · deep feature extraction · feature selection

Abstract

Brain tumors are among the important neurological diseases that require early and accurate diagnosis due to their high mortality rates and serious clinical outcomes. Although Magnetic Resonance Imaging (MRI) is widely used in the evaluation of brain tumors, structural and tissue similarities between different tumor types can complicate the diagnostic process. This increases the need for computer-aided decision systems. This study conducted a comparative analysis of machine learning approaches supported by mRMR-based feature selection and hyperparameter optimization for determining the optimal number of features in MRI-based brain tumor classification. Features extracted from MRI images belonging to glioma, meningioma, no tumor, and pituitary tumor classes were used in the study. The baseline and optimized performances of KNN, SVM, RF, XGBoost, and LightGBM algorithms were evaluated based on accuracy, precision, recall, F1-Score, and runtime criteria. After optimization, the accuracy of the KNN algorithm increased from 81.10% to 88.13%, while the accuracy of the SVM algorithm increased from 88.57% to 92.75%. The SVM classifier exhibited greater performance relative to other approaches, attaining the best accuracy of 92.75% and F1-Score of 92.73% on the test set.

Özet

Beyin tümörleri, yüksek ölüm oranları ve ciddi klinik sonuçları nedeniyle erken ve doğru tanı gerektiren önemli nörolojik hastalıklar arasındadır. Manyetik Rezonans Görüntüleme (MRG), beyin tümörlerinin değerlendirilmesinde yaygın olarak kullanılsa da farklı tümör tipleri arasındaki yapısal ve doku benzerlikleri tanı sürecini karmaşıklaştırabilir. Bu durum, bilgisayar destekli karar sistemlerine olan ihtiyacı artırmaktadır. Bu çalışma, MRG tabanlı beyin tümörü sınıflandırmasında optimal özellik sayısını belirlemek için mRMR tabanlı özellik seçimi ve hiperparametre optimizasyonu ile desteklenen makine öğrenme yaklaşımlarının karşılaştırmalı bir analizini gerçekleştirmiştir. Çalışmada, gliom, menenjiom, normal ve hipofiz tümörü sınıflarına ait MRG görüntülerinden çıkarılan özellikler kullanılmıştır. KNN, SVM, RF, XGBoost ve LightGBM algoritmalarının temel ve optimize edilmiş performansları doğruluk, hassasiyet, geri çağırma, F1-Skoru ve çalışma süresi kriterlerine göre değerlendirilmiştir. Optimizasyon sonrasında, KNN algoritmasının doğruluğu %81.10'dan %88.13'e, SVM algoritmasının doğruluğu ise %88.57'den %92.75'e yükselmiştir. SVM sınıflandırıcı, diğer yaklaşımlara kıyasla daha yüksek performans sergileyerek test setinde %92.75 doğruluk ve %92.73 F1-Skorunu elde etmiştir.

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Cite This Article

Altun, V. C., Canbay, Y. (2026). Hybrid Deep Feature Extraction and mRMR-Based Optimization-Assisted Machine Learning Approaches in MRI-Based Brain Tumor Classification. Turkish Journal of Science and Technology, Volume: 21 Issue: 2. https://doi.org/10.55525/tjst.1938123

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