Artifical intelligence with optimal deep learning enabled automated retinal fundus image classification model

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. Earlie...

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Bibliographic Details
Published in:Expert systems 2022-12, Vol.39 (10), p.n/a
Main Authors: Gupta, Indresh Kumar, Choubey, Abha, Choubey, Siddhartha
Format: Article
Language:English
Subjects:
Age related diseases
Algorithms
Artificial intelligence
Artificial neural networks
Automation
Classification
computer aided diagnosis
Computer simulation
Deep learning
Diabetes
Diabetic retinopathy
Eye diseases
fundus images
Image analysis
Image classification
Image contrast
Image enhancement
Image processing
Image segmentation
Inspection
Machine learning
Medical imaging
retinal diseases
Retinal images
RMSProp optimizer
Vision
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Summary: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%.
ISSN:0266-4720
1468-0394
DOI:10.1111/exsy.13028