ItemArticle

Diabetic retinopathy detection via deep learning based dual features integrated classification model

Title
Diabetic retinopathy detection via deep learning based dual features integrated classification model
Creator
Devi, T.M.; Karthikeyan, P.; Muthu Kumar, B.; Manikandakumar, M.
Description
Background: The primary recognition of diabetic retinopathy (DR) is a pivotal requirement to prevent blindness and vision impairment. This deadly condition is identified by highly qualified professionals by examining colored retinal images. Objective: The physical diagnostics for this condition was time-consuming and prone to fault. The development of computer-vision based intelligent systems has develop a main research area to effectually diagnosis the pathologies from an image. Methods: In this research, a novel Deep learning based Dual Features Integrated classification (DD-FIC) framework is designed to detect the DR from a color retinal image. Initially, the fundus images are denoised by Wavelet integrated Retinex (WIR) algorithm to remove the noise artifacts which provide high contrast image. This DD-FIC model contains two phases of feature extraction module to evaluation of several retinal areas. Initially, global features of the fundus image are retrieved by the assist of attention fused efficient model, whereas the attention module dynamically highlights the important features. Afterwards, the segmented retinal vessels data is converted into features for learning the local features. Results: Finally, the collective of features is processed into the Random Forest based feature selection model for the optimal prediction with five different classes using multi-class support vector machine (MCSVM). The efficacy of the proposed DD-FIC framework is estimated by Kaggle dataset with the detection accuracy of 98.6%. Conclusions: The proposed framework rises the accuracy of 1.54%, 3.65%, 13.79% and 6.28% for Multi-channel CNN, CNN, VGG NiN and Shallow CNN respectively. The Author(s) 2024.
Source
Technology and Health Care;Volume;33;Issue;2;pp.1066-1080
Date
01-01-2025
Publisher
SAGE Publications Ltd
Subject
diabetic retinopathy deep learning; global features; local features; random forest; wavelet based retinex algorithm
Coverage
Devi T.M., Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, 600062, India; Karthikeyan P., Department of Information Technology, Thiagarajar College of Engineering, Tamil Nadu, Thiruparankundram, 625015, India; Muthu Kumar B., Department of School of Computing and Information Technology, REVA University, Karnataka, Bengaluru, 560064, India; Manikandakumar M., Department of Computer Science and Engineering, School of Engineering & Technology, Christ University, Bengaluru, India
Rights
All Open Access; Bronze Open Access
Relation
ISSN: 9287329; CODEN: THCAE
Format
online
Language
English
Type
Article

Citation

Devi, T.M.; Karthikeyan, P.; Muthu Kumar, B.; Manikandakumar, M., “Diabetic retinopathy detection via deep learning based dual features integrated classification model,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/23119.

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