Nörolojik Görüntülemede Derin Öğrenme: Beyin Tümörü Sınıflandırması Için Yeni Bir Cnn Tabanlı Model

Year 2025, Volume: 13 Issue: 2, 457 - 474, 25.06.2025

Ammar Aslan

https://doi.org/10.33715/inonusaglik.1645318

Abstract

Beyin tümörleri, hayati fonksiyonları yöneten kritik beyin bölgelerine baskı yaparak ciddi nörolojik hasara ve ölüme neden olabildikleri için oldukça tehlikelidir. Radyolojik görüntülerin değerlendirilmesinde karşılaşılan insan hatası gibi karmaşık yapılar ve sınırlamalar, bu tümörlerin tespitinde zorluklar yaratmaktadır. Evrişimsel sinir ağları (CNN'ler), derin öğrenme (DL) yöntemlerinden biridir ve özellikle görsel verilerin analizinde yaygın olarak kullanılmaktadır. CNN'lerin beyin tümörlerini tespit etmedeki avantajı, görüntülerden özellikleri otomatik olarak öğrenebilmeleri ve sınıflandırma doğruluğunu artırarak insan hatasını en aza indirebilmeleridir. Bu çalışmada, manyetik rezonans görüntüleme (MRI)’de elde edilen görüntüler kullanılarak beyin tümörü teşhisi için yeni bir CNN tabanlı model önerilmiştir. Glioma, menenjiyom, normal beyin ve hipofiz tümörü olmak üzere dört kategoriye ayrılmış 3.096 MRI görüntüsünden oluşan bir veri seti kullanılarak önemki bir sınıflandırma başarısı elde edilmiştir. Geliştirilen model, tümör tespitinde genel olarak %93 doğruluk oranına ulaşmıştır. Özellikle hipofiz tümörlerinin tespitinde %96 başarı ve %95 F1 skoru ile büyük başarı gösterilmiştir. Bu çalışma, DL'nin tıbbi görüntü analizinde önemli bir potansiyele sahip olduğunu göstermektedir. Geliştirilen model, sağlık hizmetlerindeki teşhis süreçlerinde doğruluğu hızlandırma ve artırma potansiyeline sahiptir.

Keywords

Beyin Tümörü , Derin Öğrenme , Evrişimsel Sinir Ağları , Hastalık Tespiti , MRI Sınıflandırması

DEEP LEARNING IN NEUROLOGICAL IMAGING: A NOVEL CNN-BASED MODEL FOR BRAIN TUMOR CLASSIFICATION TÜRKİYE AND HEALTH RISK ASSESSMENT

Abstract

Brain tumors can cause serious neurological damage and death by putting pressure on critical brain regions that manage vital functions. Given the complex structures in the brain, human error in the evaluation of radiological images can create difficulties in the detection of these tumors. Convolutional neural networks (CNNs) are type of deep learning (DL) and are widely used, especially for analyzing visual data. The advantage of CNNs in detecting brain tumors is that they can automatically learn features from images and minimize human error by increasing the classification accuracy. In this study, a unique CNN-based model is proposed for brain tumor diagnosis using magnetic resonance imaging (MRI) images. A high classification score was obtained using a dataset consisting of 3096 MRI images divided into four categories: glioma, meningioma, normal brain, and pituitary tumor. The model achieved an overall 93% accuracy rate in tumor detection. In particular, great success was seen for the detection of pituitary tumors with 96% precision and a 95% F1 score. This study demonstrates that DL has significant potential in medical image analysis. The novelty of our approach lies in designing a lightweight CNN architecture from scratch that achieves high accuracy without relying on transfer learning, while requiring significantly fewer computational resources than traditional deep architectures.

Keywords

Brain Tumor , Convolutional Neural Networks , Deep Learning , Disease Detection , MRI Classification

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