Convolutional neural network for multi-class classification of diabetic eye disease

[thumbnail of eai.16-12-2021.172436.pdf]
eai.16-12-2021.172436.pdf - Published Version (4MB) | Preview
Available under license: Creative Commons Attribution

Sarki, Rubina ORCID: 0000-0001-5018-9567, Ahmed, Khandakar ORCID: 0000-0003-1043-2029, Wang, Hua ORCID: 0000-0002-8465-0996, Zhang, Yanchun ORCID: 0000-0002-5094-5980 and Wang, Kate ORCID: 0000-0001-5208-1090 (2021) Convolutional neural network for multi-class classification of diabetic eye disease. EAI Endorsed Transactions on Scalable Information Systems, 9 (4). ISSN 2032-9407


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. Multiclass 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%.

Dimensions Badge

Altmetric Badge

Item type Article
DOI 10.4108/eai.16-12-2021.172436
Official URL
Subjects Current > FOR (2020) Classification > 4602 Artificial intelligence
Current > Division/Research > Institute for Sustainable Industries and Liveable Cities
Keywords diabetic eye disease, DED, eye disorders, diabetes, neural network
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login