Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Devi, T.M.; Karthikeyan, P.; Muthu Kumar, B.; Manikandakumar, M. Title A name given to the resource Diabetic retinopathy detection via deep learning based dual features integrated classification model Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Technology and Health Care;Volume;33;Issue;2;pp.1066-1080 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1177/09287329241292939" target="_blank" rel="noreferrer noopener">https://doi.org/10.1177/09287329241292939</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105001450752?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105001450752?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Devi T.M., Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&amp;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 &amp; Technology, Christ University, Bengaluru, India Description An account of the resource 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. Subject The topic of the resource diabetic retinopathy deep learning; global features; local features; random forest; wavelet based retinex algorithm Publisher An entity responsible for making the resource available SAGE Publications Ltd Relation A related resource ISSN: 9287329; CODEN: THCAE Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Bronze Open Access Format The file format, physical medium, or dimensions of the resource online