Diyabetik Retinopati Teşhisi için Fundus Görüntülerinin Derin Öğrenme Tabanlı Sınıflandırılması

Günümüzde en yaygın körlük nedenlerinden biri olan Diyabetik Retinopati (DR), gözün retina ağ tabakasında yer alan kan damarlarında diyabete bağlı olarak oluşan hasarlanmalardır. Hastaların görme yetisini kaybetmemesi için DR’nin erken teşhis ve tedavisi hayati önem taşımaktadır. Bu çalışmada, DR’nin erken teşhis ve tedavisi için fundus görüntüleri kullanılarak derin öğrenme tabanlı bir model geliştirilmiştir. Geliştirilen model iki aşamadan oluşmaktadır. İlk aşamada, modelin aşırı öğrenmesinin engellenebilmesi için fundus görüntülerine iki boyutlu sinyal işleme teknikleri uygulanmıştır. İkinci aşamada, derin öğrenme tekniklerinden Evrişimli Sinir Ağı (ESA) ve transfer öğrenmesi yöntemleri kullanılarak sınıflandırma modeli oluşturulmuştur. Modelin eğitiminde 5100 fundus görüntü verisi kullanılmıştır. Elde edilen model sağlıklı (DR yok), hafif Non-Proliferatif DR (NPDR), orta NPDR, şiddetli NPDR ve Proliferatif DR (PDR) gibi 5 sınıfı içeren 900 fundus görüntü verisi üzerinde test edilmiştir. Modelin sağlamlığı 10-kat çapraz doğrulama yöntemi kullanılarak doğrulanmıştır. Önerilen modelin sınıflandırma performansı %97.8 olarak ölçülmüştür. Ayrıca, modelin sınıflandırma performansı literatürde yer alan üç model ile kıyaslanmıştır. Elde edilen sonuçlar, önerilen modelin, DR’yi teşhis etmek için çok etkili ve başarılı olduğunu göstermektedir.

Anahtar Kelimeler:

Diyabetik retinopati, Derin öğrenme, Fundus, Sınıflandırma, Evrişimli sinir ağı, Transfer öğrenme

Deep Learning-based Classification of Fundus Images for the Diagnosis of Diabetic Retinopathy

Diabetic Retinopathy (DR), one of the most common causes of blindness today, is damage to the blood vessels in the retinal mesh layer of the eye due to diabetes. Early diagnosis and treatment of DR is vital so that patients do not lose their sight. In this study, a deep learning-based model is developed using fundus images for the early diagnosis and treatment of DR. The developed model consists of two stages. In the first stage, two-dimensional signal processing techniques are applied to the fundus images to prevent overfitting of the model. In the second stage, the classification model is created by using deep learning techniques, Convolutional Neural Network (CNN) and transfer learning methods. 5100 fundus image data is used in the training of the model. The validity of the obtained model is tested on 900 fundus image data containing 5 classes such as No DR, mild Non-Proliferative DR (NPDR), moderate NPDR, severe NPDR and Proliferative DR (PDR). The robustness of the model is verified using the 10-fold cross validation method. The classification performance of the proposed model is measured as 97.8%. Moreover, the classification performance of the model is compared with the three models in the literature. The obtained results show that the proposed model is very effective and successful for diagnosing DR.

Keywords:

Diabetic retinopathy, Deep learning, Fundus, Classification, Convolutional neural network, Transfer learning,

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