Derin Öğrenmeye Karşı Makine Kullanarak Diyabetik Retinopati Teşhisi

Year 2023, , 301 - 313, 31.08.2023

Nehad Ramaha , Shuhad Imad

https://doi.org/10.31590/ejosat.1263514

Abstract

Diyabetik retinopati hastalığı dünya çapında milyonlarca insanı etkilemektedir. Diyabetik hastalığın bir komplikasyonu olarak kabul edilir ve göz görüşünü etkileyebilir. Hekimler bu hastalığı tıbbi göz muayenesi ile tespit edebilirler. Nihai kararı vermek için birçok görüntünün işlenmesi gerekir. Neyse ki, bilgisayar destekli karar destek sistemleri, doktorların daha az çaba ve zaman harcayarak doğru kararlar vermelerine yardımcı olabilir. Bu çalışmada, güncel diyabetik retinopati bilgisayar destekli sistemlerin bir derlemesi sunulmaktadır. Çalışma, diyabetik retinopati tespiti için makine öğrenimi veya derin öğrenme yaklaşımlarının kullanıldığı çalışmaları içermektedir. Bu makale, önerilen metodoloji, kullanılan veri seti, elde edilen sonuçlar ve değerlendirme açısından önceki tüm çalışmaları karşılaştırmaktadır. Çalışma ayrıca mevcut diyabetik retinopati veri setlerini de karşılaştırdı. Sonuç olarak, derin öğrenmeye dayalı yöntemlerin en iyi performansı gösterdiğini gördük. Ayrıca, diyabetik retinopati evrelerinin kategorik sınıflandırması, hastalık tespitinde ikili sınıflandırma yapmaktan daha iyiydi. Bu çalışma, araştırmacıların gelecekteki çalışmalarında en iyi metodolojileri ve veri kümelerini seçmelerine yardımcı olur.

Keywords

Diyabetik Retinopati, Makine Öğrenimi, Derin Öğrenme, Kan Damarları, Görüntü İşleme, Görüntü Sınıflandırma

Diabetic Retinopathy Diagnosis Using Machine Versus Deep Learning

Abstract

Diabetic retinopathy disease affects millions of people around the world. It is considered a complication of diabetic disease and can affect eye vision. Physicians can detect this disease by medical eye examination. Many images need to be processed in order to make the final decision. Fortunately, computer-aided decision support systems can help physicians make accurate decisions with less effort and time. In this study, a review of the current diabetic retinopathy computer-aided systems is introduced. The study includes studies using machine learning or deep learning approaches for diabetic retinopathy detection. This paper compares all those previous studies in terms of the proposed methodology, the used dataset, the acquired results, and the evaluation. The study also compared the current diabetic retinopathy datasets. As a result, we found that the methods that were based on deep learning had the best performance. Besides, the categorical classification of diabetic retinopathy stages was better than doing a binary classification of disease detection. This study helps researchers in their future work to select the best methodologies and datasets.

Keywords

Diabetic Retinopathy, Machine Learning, Deep Learning, Blood Vessels, Image Processing, Image Classification

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