Title: Detection of diabetic retinopathy severity from fundus images: DCNN

Authors: T. Senthil Kumar; R. Muthalagu; L. Mohana Sundari; M. Nalini

Addresses: GRT Institute of Engineering and Technology, Tiruttani, India ' Department of Electronics and Communication Engineering, Agni College of Technology, Chennai, India ' Department of Electronics and Communication Engineering, Saveetha Engineering College (Autonomous), Chennai, India ' Department of Electronics and Instrumentation Engineering, Sri Sairam Engineering College, Chennai, India

Abstract: Diabetes retinopathy is a frequent diabetic complication that damages the retina and, if left untreated, may lead to blindness. The exponential rise in the number of diabetics throughout the globe has resulted in an equivalent rise in the number of diabetic retinopathy (DR) patients, one of the most serious consequences of diabetes. The goal of this research is to develop a hybrid solution approach for identifying diabetic retinopathy using retinal fundus pictures. The process of retinal vascular segmentation is critical for detecting a variety of eye disorders, such as the effects of diabetes on the eyes, also known as diabetic retinopathy. Morphologically based operations were used for the autoextraction of retinal blood vessels. Wavelet decomposition and back propagation neural networks were used to extract retinal vascular characteristics and evaluate the dataset that was used for this article. Morphologically based operations were also used for autoextraction of retinal blood vessels.

Keywords: diabetic retinopathy; fundus images; retina; deep learning; image processing.

DOI: 10.1504/IJMEI.2025.145042

International Journal of Medical Engineering and Informatics, 2025 Vol.17 No.2, pp.195 - 206

Received: 19 May 2022
Accepted: 11 Aug 2022
Published online: 18 Mar 2025 *

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