Supervised learning software model for the diagnosis of diabetic retinopathy. (27th October 2022)

• Record Type: Journal Article
• Authors: Padmapriya, M.; Pasupathy, S.
• Is Part Of: International journal of computational vision and robotics. Volume 13:Number 1(2023)
• Page Start: 116
• Page End: 132
• Publication Date: 2022-10-27
• Subjects: diabetic retinopathy -- fundus imaging -- grey level features -- invariant moments -- vessel segmentation; Computer vision -- Periodicals; Robotics -- Periodicals; Artificial intelligence -- Periodicals; 006.3705
• Languages: English
• ISSNs: 1752-9131

Abstract

Diabetic retinopathy (DR) is the leading cause of eye diseases and vision loss for diabetic affected people. Due to the damage of retinal blood vessels, diabetic patients often suffer from DR. So the retinal blood vessel segmentation plays a crucial role in the diagnosis of DR. We can prevent vision loss or blindness problems if the diagnosis happens during the early stages. Early diagnosis and initial investigation would help lower the risk of vision loss by 50%. This article exploits the supervised classification approach to detect blood vessels by applying features such as grey level and invariant moments. The image pre-processing and blood vessel segmentation are the two essential steps are used in this study, along with the proposed classification framework using neural network models. Two publicly available retinal image datasets, such as DRIVE and STARE, are used to assess the proposed supervised classification framework. The suggested supervised classification methodology in this study attains the average retinal blood vessel segmentation accuracy of 93.94% in the DRIVE dataset and 95.00% in the STARE dataset.