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 Articles 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/. Title A name given to the resource Ar-HGSO: Autoregressive-Henry Gas Sailfish Optimization enabled deep learning model for diabetic retinopathy detection and severity level classification Subject The topic of the resource Deep Convolutional Neural Network; Diabetic retinopathy; Entropy Weighted and kernalized Power k-Means Clustering; Henry Gas Solubility Optimization model; Shepard Convolutional Neural network Description An account of the resource Diabetic Retinopathy (DR) is one the most important problems of diabetics and it directs to the main cause of blindness. When proper treatment is afforded for DR patients, almost 90% of patients are protected from visual damage. DR does not produce any symptoms at the initial phase of the disease, thus various physical assessments, namely pupil dilation, visual acuity test, and so on are needed for DR disease detection. It is more complex to detect the DR during manual testing, because of the variations and complications of DR. The early detection and appropriate treatment assist to prevent vision loss for DR patients. Thus, it is very indispensable to categorize the levels and severity of DR for recommendation of essential treatment. In this paper, Autoregressive-Henry Gas Sailfish Optimization (Ar-HGSO)-based deep learning technique is proposed for DR detection and severity level classification of DR and Macular Edema (ME) based on color fundus images. The segmentation process is more essential for proper detection and classification process, which segments the image into various subgroups. The Deep Learning approach is utilized for effective identification of DR and severity classification of DR and ME. Moreover, the deep learning technique is trained by the designed Ar-HGSO scheme for obtaining better performance. The performance of the devised technique is evaluated using the IDRID dataset and DDR dataset. The introduced Ar-HGSO-based deep learning approach obtained better performance than other existing DR detection and classification techniques with regards to testing accuracy, sensitivity, and specificity of 0.9142, 0.9254, and 0.9142 using the IDRID dataset. 2022 Elsevier Ltd Creator An entity primarily responsible for making the resource Elwin J.G.R.; Mandala J.; Maram B.; Kumar R.R. Source A related resource from which the described resource is derived Biomedical Signal Processing and Control, Vol-77 Publisher An entity responsible for making the resource available Elsevier Ltd Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.bspc.2022.103712" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.bspc.2022.103712</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130616233&amp;doi=10.1016%2Fj.bspc.2022.103712&amp;partnerID=40&amp;md5=e21a62bf8dd88d76f44b1c203bc8a056" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130616233&amp;doi=10.1016%2fj.bspc.2022.103712&amp;partnerID=40&amp;md5=e21a62bf8dd88d76f44b1c203bc8a056</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 17468094 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Elwin J.G.R., Associate Professor, Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore, India; Mandala J., Department of Computer Science and Engineering, Christ University, Bangalore, India; Maram B., Associate Professor, Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Himachal Pradesh, India; Kumar R.R., Professor, Department of Information Technology, Sri Krishna College Technology, Coimbatore, India