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ISSN 1004-9037
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Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
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      1 Jan 2024, Volume 39 Issue 1   
    Article

    MULTIPLE EYE DISEASE DETECTION USING CONVOLUTIONAL NEURAL NETWORK
    Santhosh Kumar B N1, Dr. G N K Suresh Babu2
    Journal of Data Acquisition and Processing, 2024, 39 (1): 1310-1317 . 

    Abstract

    Among the most important systems in the body is the eyes. Although their small stature, humans are unable to imagine existence without it. The human optic is safe against dust particles by a narrow layer called the conjunctiva. It prevents friction during the opening and shutting of the eye by acting as a lubricant. A cataract is an opacification of the eye's lens. There are various forms of eye problems. Because the visual system is the most important of the four sensory organs, external eye abnormalities must be detected early. The classification technique can be used in a variety of situations. To examine multiple eye disease detection algorithm based on Optical Coherence Tomography (OCT) scans using Convolution Neural Network (CNN). The proposed work has been deployed with convolution neural networks performed on the OCT image from authenticated data set and accuracy of 91% was achieved using 5-fold cross validation. The highest AUC value for the normal class observed as 1. More than 90% AUC in predicting eye disease for all the classes has been attained using the proposed approach.

    Keyword

    Cataract, Convolution Neural Network, multiple eye disease, Optic, Optical Coherence Tomography


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ISSN 1004-9037

         

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