Yeni Bir Beyin Tümörü Tespiti Yaklaşımı: Derin Öğrenme ve Destek Vektör Makinelerinin Entegre Edilmesi

Year 2026, Volume: 28 Issue: 82, 157 - 162, 27.01.2026

Ezgi Özer

https://doi.org/10.21205/deufmd.2026288220

Abstract

Beyin tümörleri insanlarda en sık görülen ölüm nedenleri arasındadır. Beyin kanserlerinin erken ve doğru tespiti etkili tedavi için kritik öneme sahiptir. Bilgisayarlı tomografi, manyetik rezonans görüntüleme, X-ışınları ve ultrason gibi görüntüleme teknikleri hastalık uzmanları tarafından ön referans olarak kullanılmaktadır. Hastalıkları erken teşhis etmek, uzmanların yoğunluğunu azaltmak ve teşhis hatalarını en aza indirmek için sağlık alanında farklı öğrenme stratejileri kullanılmaktadır. Beyin araştırmalarında görüntü işleme çalışmaları son yıllarda makine öğrenmesi ve derin öğrenme modellerinin geliştirilmesi sayesinde başarılı bulgular sağlamaya başlamıştır. Bu çalışmada literatürdeki çalışmalara bir yenilik olarak, önceden eğitilmiş CNN tabanlı özellikler çıkarımı yapılan, SVM tabanlı farklı çekirdeklerle sınıflandırma yapılan bir hibrit algoritma önerilmiştir. Sonuç olarak beyin tümörleri %98 sınıflandırma performansı ile tespit edilmiştir.

Keywords

Tümör Tespiti , Özellik Çıkarımı , Önceden Eğitilmiş Model , Destek Vektör Makineleri

Project Number

1059B141900679

A Novel Brain Tumor Detection Approach: Integrating Deep Learning and Support Vector Machines

Abstract

Brain tumors are among the most common causes of death in humans. Early and accurate detection of brain cancers is critical for effective treatment. Imaging techniques such as computed tomography, magnetic resonance imaging, X-rays, and ultrasound are used as a preliminary reference by illness experts. Different learning strategies have been employed in the field of health to diagnose diseases early, reduce the intensity of experts, and minimize diagnostic errors. Image processing studies in brain research have begun to provide successful findings in recent years, thanks to the developed of machine learning and deep learning models. In this study, as a novelty to the studies in the literature, a hybrid algorithm is proposed that features were extracted with pre-trained based CNN, classification was made with SVM based different kernels. As a result, the brain tumors were detected with 98% classification performance.

Keywords

Tumor Detection , Feature Extraction , Pre-Trained Models , Support Vector Machine

Project Number

1059B141900679

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