Vol.67, No.1, 2021, pp.411-426, doi:10.32604/cmc.2021.013390
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:
(This article belongs to this Special Issue: Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
Received 05 August 2020; Accepted 01 November 2020; Issue published 12 January 2021
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.
Multi-categorical classification; deep neural networks; glaucoma; age-related macular degeneration; diabetic retinopathy
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.
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