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Journals / Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi / 2026 / Cilt: 40 Sayı: 1

Detection of Retinal Diseases from Fundus Images Using Deep Learning and Adaptive Histogram Equality

Derin Öğrenme ve Adaptif Histogram Eşitleme Kullanarak Retinal Hastalıkların Fundus Görüntülerinden Tespiti

Ali Emre Gök(NEVSEHIR HACI BEKTAS VELI UNIVERSITY)0009-0009-8292-2132
Sakir Tasdemır(SELCUK UNIVERSITY)0000-0002-2433-246X
Pages
123–135
Publication Date
2024-04-30
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These results show that deep learning algorithms can be used in the diagnosis of eye diseases.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Recently, various eye diseases such as cataracts, diabetic retinopathy, glaucoma, macular edema, myopia, and astigmatism have been seen frequently.
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Key Findings & Data:Because of these reasons, computer-aided diagnosis systems that can diagnose accurately are required.
💡
Conclusion & Impact:These results show that deep learning algorithms can be used in the diagnosis of eye diseases.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Derin öğrenme · Diyabetik retinopati · Katarakt

Abstract

Recently, various eye diseases such as cataracts, diabetic retinopathy, glaucoma, macular edema, myopia, and astigmatism have been seen frequently. Cataracts, diabetic retinopathy, and glaucoma cause blurred vision, loss of vision, and blindness in cases where they are left untreated and undiagnosed. Lack of experts and equipment, hardware problems, and erroneous decisions made by experts cause problems in the diagnosis process. Because of these reasons, computer-aided diagnosis systems that can diagnose accurately are required. Deep learning algorithms performed well in the field of health, recently. These results show that deep learning algorithms can be used in the diagnosis of eye diseases. In this study, various CNN models were used for classifying eye diseases such as cataracts, diabetic retinopathy, and glaucoma from fundus images. In the image preprocessing stage, the Contrast Limited Adaptive Histogram Equalization method was used. Experimental results demonstrate that VGG16 was the most successful model among the evaluated models in this study and the Contrast Limited Adaptive Histogram Equalization method increased the performance.

Özet

Günümüzde, katarakt, diyabetik retinopati, glokom, maküler ödem, miyop ve astigmat gibi çeşitli göz hastalıkları sıklıkla görülmektedir. Bu hastalıklardan katarakt, diyabetik retinopati ve glokom, teşhis ve tedavi edilmedikleri durumlarda, bulanık görmeye, görme kaybına ve hatta körlüğe neden olmaktadır. Uzman ve donanım eksikliği, donanımsal problemler ve uzmanlarca verilen hatalı kararlar gibi çeşitli nedenlerle, teşhis aşamasında sorunlarla karşılaşılmaktadır. Bu nedenlerden dolayı, Bilgisayar Destekli Teşhis sistemlerine ihtiyaç duyulmaktadır. Son zamanlarda, derin öğrenme algoritmalarıyla gerçekleştirilen çalışmalarda, başarılı sonuçlar elde edilmiştir. Bu başarılı sonuçlar, derin öğrenmenin, göz hastalıklarının teşhisinde kullanılabileceğini göstermektedir. Bu çalışmada, çeşitli CNN modelleri kullanılarak, fundus görüntüleri üzerinden, katarakt, diyabetik retinopati ve glokom gibi göz hastalıklarının sınıflandırılması gerçekleştirilmiştir. Fundus görüntülerinde, Kontrast Sınırlı Adaptif Histogram Eşitleme yöntemi kullanılmıştır. Deneysel sonuçlar, VGG16 modelinin, bu üç model arasında en başarılı model olduğunu ve Kontrast Sınırlı Adaptif Histogram Eşitleme yönteminin de performansı arttırdığını göstermektedir.

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Cite This Article

Gök, A. E., Tasdemır, S. (2024). Detection of Retinal Diseases from Fundus Images Using Deep Learning and Adaptive Histogram Equality. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, Cilt: 40 Sayı: 1, 123–135. https://ajindex.com/articles/1266747

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