Fundus Görüntülerinden Derin Öğrenme Teknikleri ile Glokom Hastalığının Tespiti

Glokom optik siniri etkileyen ve erken teşhis edilmediği durumlarda kısmi ya da kalıcı körlüğe neden olan bir retina hastalığıdır. Zamanla görme kaybına neden olan glokomun teşhisi için doktorlar fundus görüntülerini kullanmaktadır. Glokomun etken teşhisi oldukça önemlidir. Bu çalışmada, fundus görüntülerinden glokom tespiti için Evrişimli Sinir Ağları (ESA) modellerinden olan AlexNet, ResNet-18, VGG16, SqueezeNet ve GoogleNet kullanılmıştır. Kullanılan mimariler için elde edilen sonuçlar doğruluk, duyarlılık, özgüllük ve f1-ölçütü değerleri olmak üzere farklı performans metriklerine göre değerlendirilmiştir. Sonuçlara göre test veri kümesinde en iyi duyarlılık değeri %97.96 ile VGG16 tarafından elde edildiği, özgüllük, doğruluk ve f1-ölçütü için en iyi değerlerin ise sırasıyla %98.97, %97.98 ve %98 ile GoogleNet olduğu tespit edilmiştir.

Anahtar Kelimeler:

Evrişimsel Sinir Ağları, Fundus görüntüleri, Glokom, Yapay Zekâ

Detection of Glaucoma Disease with Deep Learning Techniques from Fundus Images

Glaucoma is a retinal disease that affects the optic nerve and causes partial or permanent blindness if not diagnosed early. To diagnose glaucoma, which causes vision loss over time, doctors use fundus images. The causative diagnosis of glaucoma is very important. In this study, Convolutional Neural Networks (CNN) models AlexNet, ResNet-18, VGG16, SqueezeNet, and GoogleNet were used for glaucoma detection from fundus images. The results obtained for the architectures used were evaluated according to different performance metrics such as accuracy, sensitivity, specificity, and f1-criterion values. According to the results, it was determined that the best sensitivity value in the test dataset was obtained by VGG16 with 97.96%, and the best values for specificity, accuracy, and f1-criterion were GoogleNet with 98.97%, 97.98%, and 98%, respectively.

Keywords:

Evrişimsel Sinir Ağları, Fundus görüntüleri, Glokom, Yapay Zekâ,

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