A deep neural network and machine learning approach for retinal fundus image classification

• Published in: Healthcare analytics (New York, N.Y.) N.Y.), 2023-11, Vol.3, p.100140, Article 100140
• Main Author: Thanki, Rohit
• Format: Article
• Language: English
• Subjects: Classification; Deep neural network; Glaucoma; Machine learning; Retinal image
• ISSN: 2772-4425; 2772-4425
• DOI: 10.1016/j.health.2023.100140

Diabetes is a common chronic disease and a major public health problem approaching epidemic proportions globally. People with diabetes are more likely to suffer from glaucoma than people without diabetes. Glaucoma can lead to loss of vision if not diagnosed at an early stage. This study proposes an intelligent computer-aided triage system with a deep neural network and machine learning to develop and analyze color retinal fundus images and classify glaucomatous retinal images. Deep features of retinal images from the fundus retinal image are extracted using a deep neural network, and the classification of features is performed and analyzed using different machine learning classifiers. Experimental results show that the combination of deep neural network and logistic regression-based classifier outperforms all existing glaucomatous triage systems, improving classification accuracy, sensitivity, and specificity. •Diabetes is a common chronic disease and a major public health problem.•People with diabetes are more likely to suffer from glaucoma than people without diabetes.•We propose an intelligent computer-aided triage system to analyze and classify glaucomatous retinal images.•Experimental results show that the proposed system outperforms existing systems.•The proposed method improves classification accuracy, sensitivity, and specificity.