Artifical intelligence with optimal deep learning enabled automated retinal fundus image classification model. Issue 10 (26th May 2022)

• Record Type: Journal Article
• Authors: Gupta, Indresh Kumar; Choubey, Abha; Choubey, Siddhartha
• Other Names: Dhiman Gaurav guestEditor.; Nagar Atulya K. guestEditor.; Gupta Deepak guestEditor.
• Is Part Of: Expert systems. Volume 39:Issue 10(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-05-26
• Subjects: computer aided diagnosis -- deep learning -- fundus images -- retinal diseases -- RMSProp optimizer; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.13028
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Diabetic retinopathy (DR) and age related macular degeneration (AMD) becomes widespread microvascular illness among diabetic patients. Traditional retinal fundus image classification requires visual inspection by the professionals, which is time consuming and requires expert's knowledge. Earlier identification of retinal diseases is essential to delay or avoid vision deterioration and vision loss. The recently developed artificial intelligence (AI) and deep learning (DL) models can be employed for accurate retinal image classification. With this motivation, this study designs a new artificial intelligence with optimal deep convolutional neural network (AI‐ODCNN) technique for retinal fundus image classification. Primarily, the proposed model uses the Gaussian Blur based noise removal and contrast enhancement technique (CLAHE) based contrast enhancement technique to pre‐process the retinal fundus image. In addition, morphology and contour based image segmentation is performed. Moreover, the deep CNN with RMSProp Optimizer is employed for retinal fundus image classification. A wide range of simulations was performed on the automated retinal image analysis and structured analysis of the retina and the outcomes are examined with respect to various measures. The simulation outcomes ensured the better performance of the proposed approach related to other recent algorithms with maximum accuracy of 96.47%.