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Classification of Fundus Images Based on Deep Learning for Detecting Eye Diseases

Nakhim Chea1, Yunyoung Nam2,*

1 Department of ICT Convergence Rehabilitation Engineering, Soonchunhyang University, Asan, 31538, Korea
2 Department of Computer Science and Engineering, Soonchunhyang University, Asan, 31538, Korea

* Corresponding Author: Yunyoung Nam. Email: email

(This article belongs to this Special Issue: Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)

Computers, Materials & Continua 2021, 67(1), 411-426.


Various techniques to diagnose eye diseases such as diabetic retinopathy (DR), glaucoma (GLC), and age-related macular degeneration (AMD), are possible through deep learning algorithms. A few recent studies have examined a couple of major diseases and compared them with data from healthy subjects. However, multiple major eye diseases, such as DR, GLC, and AMD, could not be detected simultaneously by computer-aided systems to date. There were just high-performance-outcome researches on a pair of healthy and eye-diseased group, besides of four categories of fundus image classification. To have a better knowledge of multi-categorical classification of fundus photographs, we used optimal residual deep neural networks and effective image preprocessing techniques, such as shrinking the region of interest, iso-luminance plane contrast-limited adaptive histogram equalization, and data augmentation. Applying these to the classification of three eye diseases from currently available public datasets, we achieved peak and average accuracies of 91.16% and 85.79%, respectively. The specificities for images from the eyes of healthy, GLC, AMD, and DR patients were 90.06%, 99.63%, 99.82%, and 91.90%, respectively. The better specificity performances may alert patient in an early stage of eye diseases to prevent vision loss. This study presents a possible occurrence of a multi-categorical deep neural network technique that can be deemed as a successful pilot study of classification for the three most-common eye diseases and can be used for future assistive devices in computer-aided clinical applications.


Cite This Article

N. Chea and Y. Nam, "Classification of fundus images based on deep learning for detecting eye diseases," Computers, Materials & Continua, vol. 67, no.1, pp. 411–426, 2021.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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