A hybrid convolutional neural network model for detection of diabetic retinopathy. (13th May 2023)

• Record Type: Journal Article
• Authors: Alshawabkeh, Musa; Ryalat, Mohammad Hashem; Dorgham, Osama M.; Alkharabsheh, Khalid; Btoush, Mohammad Hjouj; Alazab, Mamoun
• Is Part Of: International journal of computer applications technology. Volume 70:Number 3/4(2023)
• Page Start: 179
• Page End: 196
• Publication Date: 2023-05-13
• Subjects: deep learning -- diabetic retinopathy -- eye diseases -- retinal diagnosis -- retinal images -- convolutional neural networks -- medical applications -- ensemble classification; Technology -- Data processing -- Periodicals; 620.00285
• Languages: English
• ISSNs: 0952-8091

Abstract

Diabetic retinopathy causes vision loss. Regular eye screening has to be done to provide the appropriate treatment and for vision loss prevention. Globally, patients with DR are increasing, which leads to work pressure on specialists and equipment. Fundus images are a key factor in effective retinal diagnosis. In this paper, a deep-learning approach is proposed to detect DR from retinal images. The proposed approach involves a combination of four effective techniques: image augmentation, contrast limited adaptive histogram equalisation, CNN and transfer learning and ensemble classification. The results show the proposed approach obtained high values of accuracy (93%), precision (95%) and recall (96%), and more stability compared with other approaches.