sis 18: e15

Research Article

Convolutional Neural Network for Multi-class Classification of Diabetic Eye Disease

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  • @ARTICLE{10.4108/eai.16-12-2021.172436,
        author={Rubina Sarki and Khandakar Ahmed and Hua Wang and Yanchun Zhang and Kate Wang},
        title={Convolutional Neural Network for Multi-class Classification of Diabetic Eye Disease},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        keywords={Diabetic Eye Disease, Deep Learning, Multi-class Classification, Image Processing},
  • Rubina Sarki
    Khandakar Ahmed
    Hua Wang
    Yanchun Zhang
    Kate Wang
    Year: 2021
    Convolutional Neural Network for Multi-class Classification of Diabetic Eye Disease
    DOI: 10.4108/eai.16-12-2021.172436
Rubina Sarki1,*, Khandakar Ahmed1, Hua Wang1, Yanchun Zhang1, Kate Wang2
  • 1: ISILC, Victoria University, Melbourne, VIC 3011, Australia
  • 2: School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC 3083, Australia
*Contact email:


Prompt examination increases the chances of effective treatment of Diabetic Eye Disease (DED) and reduces the likelihood of permanent deterioration of vision. A key tool commonly used for the initial diagnosis of patients with DED or other eye disorders is the screening of retinal fundus images. Manual detection with these images is, however, labour intensive and time consuming. As deep learning (DL) has recently been demonstrated to provide impressive benefits to clinical practice, researchers have attempted to use DL method to detect retinal eye diseases from retinal fundus photographs. DL techniques in machine learning (ML) have achieved state-of-the-art performance in the binary classification of healthy and diseased retinal fundus images while the classification of multi-class retinal eye diseases remains an open challenge. Multi- class DED is therefore considered in this study seeking to develop an automated classification framework for DED. Detecting multiple DEDs from retinal fundus images is an important research topic with practical consequences. Our proposed model was tested on various retinal fundus images gathered from the publicly available dataset and annotated by an ophthalmologist. This experiment was conducted employing a new convolutional neural network (CNN) model. Our proposed model for multi-class classification achieved a maximum accuracy of 81.33%, sensitivity of 100%, and specificity of 100%.