Beyin Bilgisayarlı Tomografi Görüntülerinde Görü Dönüştürücüler ve GAN Tabanlı Veri Artırma Kullanılarak İnme Sınıflandırması

Yıl 2025, Cilt: 37 Sayı: 1, 387 - 400, 27.03.2025

Erdem Yelken , Murat Ceylan

https://doi.org/10.35234/fumbd.1598597

Öz

Bu çalışma, inme sınıflandırması için yenilikçi bir yaklaşım sunmaktadır. Araştırmada, beyin bilgisayarlı tomografi (BT) görüntüleri kullanılarak “inme yok”, “iskemik inme” ve “hemorajik inme” olmak üzere üç farklı sınıfı ayırt etmeyi amaçlayan, dikkat mekanizmaları içeren derin öğrenme tabanlı bir yöntem olan Görü Dönüştürücüler (GD) uygulanmıştır. GD modelleri, bu çalışmada görüntü verisi sınıflandırması için güçlü ve etkili bir yöntem olarak kullanılmıştır. Modelin performansını artırmak amacıyla çeşitli eğitim stratejileri ve veri artırma teknikleri uygulanmıştır. Özellikle, GAN tabanlı SRGAN (Süper Çözünürlük GAN) ve BSRGAN (Kör Süper Çözünürlük GAN) mimarileri, veri setini genişletmek ve çeşitliliği artırmak için kullanılmıştır. Bu GAN tabanlı artırma teknikleri, modelin genel başarımını ve sınıflandırma doğruluğunu önemli ölçüde iyileştirmiştir. Görü Dönüştürücü modeli, çok sınıflı sınıflandırma görevleri kapsamında çeşitli performans ölçütleriyle kapsamlı biçimde değerlendirilmiştir. Üç sınıflı sınıflandırma görevinde model, %99,06 doğruluk, %98,18 hassasiyet, %98,94 duyarlılık ve %98,54 F1 skoru elde etmiştir. Hemorajik ve iskemik inme sınıflandırmasında modelin doğruluğu %99,78, hassasiyeti %99,02, duyarlılığı %99,66 ve F1 skoru %99,26 olarak raporlanmıştır. İkili “inme var/yok” sınıflandırmasında ise model, %98,68 doğruluk, %97,80 hassasiyet, %98,54 duyarlılık ve %98,14 F1 skoru elde etmiştir. Bu bulgular, Görü Dönüştürücüler’in hızlı ve güvenilir inme teşhisine katkı sunma potansiyelini ve tıbbi uygulamalarda karar destek sistemlerinin gelişimine önemli ölçüde katkı sağlayabileceğini göstermektedir.

Anahtar Kelimeler

İnme sınıflandırması, görü dönüştürücüler, BT görüntüleme, veri artırma, derin öğrenme

Stroke Classification in Brain Computed Tomography Images Using Vision Transformers and GAN-based Data Augmentation

Öz

This study presents an innovative approach to stroke classification. The research utilizes brain computed tomography (CT) images to distinguish between three classes: “no stroke” “ischemic stroke” and “hemorrhagic stroke” employing Vision Transformers (ViTs), a deep learning-based method incorporating attention mechanisms. In this work, ViTs were effectively applied as a powerful method for image-based classification. To enhance model performance, various training strategies and data augmentation techniques were implemented. Specifically, GAN-based architectures such as SRGAN (Super-Resolution GAN) and BSRGAN (Blind Super-Resolution GAN) were used to expand the dataset and improve its diversity. These GAN-based augmentation techniques significantly improved the model’s overall performance and classification accuracy. The Vision Transformer model was rigorously evaluated through multi-class classification tasks using a range of performance metrics. In the three-class classification task, the model achieved 99.06% accuracy, 98.18% precision, 98.94% recall, and a 98.54% F1-score. For the binary classification of ischemic vs. hemorrhagic stroke, the model reported 99.78% accuracy, 99.02% precision, 99.66% recall, and a 99.26% F1-score. In the binary classification of stroke presence, the model achieved 98.68% accuracy, 97.80% precision, 98.54% recall, and a 98.14% F1-score. These findings demonstrate the potential of Vision Transformers to assist in faster and more reliable stroke diagnosis and highlight their contribution to the development of decision support systems in medical applications.

Anahtar Kelimeler

Stroke classification, vision transformers, CT imaging, data augmentation, deep learning

Kaynakça

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