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This study proposes a novel approach to the identification of DR employing methods such as synthetic data generation, K- Means Clustering-Based Binary Grey Wolf Optimizer (KCBGWO), and Fully Convolutional Encoder-Decoder Networks (FCEDN). This is achieved using Generative Adversarial Networks (GANs) to generate high-quality synthetic data and transfer learning for accurate feature extraction and classification, integrating these with Extreme Learning Machines (ELM). The substantial evaluation plan we have provided on the IDRiD dataset gives exceptional outcomes, where our proposed model gives 99.87% accuracy and 99.33% sensitivity, while its specificity is 99. 78%. This is why the outcomes of the presented study can be viewed as promising in terms of the further development of the proposed approach for DR diagnosis, as well as in creating a new reference point within the framework of medical image analysis and providing more effective and timely treatments.<\/jats:p>","DOI":"10.1371\/journal.pone.0312016","type":"journal-article","created":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T14:08:19Z","timestamp":1733407699000},"page":"e0312016","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["DRSegNet: A cutting-edge approach to Diabetic Retinopathy segmentation and classification using parameter-aware Nature-Inspired optimization"],"prefix":"10.1371","volume":"19","author":[{"given":"Sundreen Asad","family":"Kamal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youtian","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Majdi","family":"Khalid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Majed","family":"Farrash","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3620-1395","authenticated-orcid":true,"given":"Sahraoui","family":"Dhelim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,12,5]]},"reference":[{"key":"pone.0312016.ref001","doi-asserted-by":"crossref","DOI":"10.1016\/j.diabres.2017.03.024","article-title":"IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040","volume":"128","author":"K Ogurtsova","year":"2017","journal-title":"Diabetes Res Clin Pract"},{"key":"pone.0312016.ref002","article-title":"IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045","volume":"138","author":"NH Cho","year":"2018","journal-title":"Diabetes Res Clin Pract"},{"key":"pone.0312016.ref003","article-title":"IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045","volume":"183","author":"H Sun","year":"2022","journal-title":"Diabetes Res Clin Pract"},{"key":"pone.0312016.ref004","doi-asserted-by":"crossref","DOI":"10.1186\/s12886-021-02034-x","article-title":"The effect of implementation of evidence-based eye care protocol for patients in the intensive care units on superficial eye disorders","volume":"21","author":"A Pourghaffari Lahiji","year":"2021","journal-title":"BMC Ophthalmol"},{"key":"pone.0312016.ref005","doi-asserted-by":"crossref","DOI":"10.1016\/S0161-6420(03)00475-5","article-title":"Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales","volume":"110","author":"CP Wilkinson","year":"2003","journal-title":"Ophthalmology"},{"key":"pone.0312016.ref006","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1016\/j.jfo.2020.08.009","article-title":"Survey on recent developments in automatic detection of diabetic retinopathy","author":"A Bilal","year":"2021","journal-title":"Journal Francais d\u2019Ophtalmologie"},{"key":"pone.0312016.ref007","doi-asserted-by":"crossref","first-page":"23544","DOI":"10.1109\/ACCESS.2021.3056186","article-title":"Diabetic Retinopathy Detection and Classification Using Mixed Models for a Disease Grading Database","volume":"9","author":"A Bilal","year":"2021","journal-title":"IEEE Access"},{"key":"pone.0312016.ref008","doi-asserted-by":"crossref","first-page":"108099","DOI":"10.1016\/j.compbiomed.2024.108099","article-title":"NIMEQ-SACNet: A novel self-attention precision medicine model for vision-threatening diabetic retinopathy using image data","volume":"171","author":"A Bilal","year":"2024","journal-title":"Comput Biol Med"},{"key":"pone.0312016.ref009","doi-asserted-by":"crossref","first-page":"4094","DOI":"10.3390\/electronics12194094","article-title":"EdgeSVDNet: 5G-Enabled Detection and Classification of Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images","volume":"12","author":"A. 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