{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T17:24:07Z","timestamp":1787592247005,"version":"build-2736575974"},"reference-count":50,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T00:00:00Z","timestamp":1748217600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T00:00:00Z","timestamp":1748217600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Evaluation Clinical Practice"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Over the past 20 years, researchers have concentrated on generating retinal images as a means of detecting and classifying chronic diseases. Early diagnosis and treatment are essential to avoid chronic diseases. Manually grading retinal images is time\u2010consuming, prone to errors, and lacks patient\u2010friendliness. Various Deep Learning (DL) algorithms are employed to detect chronic diseases from retinal fundus images. Also, these methods have some disadvantages, such as overfitting, computational cost, and so on.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>The proposed research aims to develop Optimized DL based system for detecting chronic diseases in retinal images and solving existing issues.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methodology<\/jats:title>\n                    <jats:p>Initially, the retinal images are pre\u2010processed to clean and organize the data. Normalization and HSI Colour Conversion are the techniques used for pre\u2010processing. Inception\u2010V3, ResNet\u2010152 and a Convolutional Vision Transformer (Conv\u2010ViT) are used to perform feature extraction. The classifier is an Optimized Hybrid Attention\u2010based Capsule Network. An optimization is included in the proposed model to increase the classifier s performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The proposed approach attains accuracies of 99.05 % and 99.15% using Diabetic Retinopathy 224\u2009\u00d7\u2009224 (2019 Data) and the APTOS\u20102019 dataset, respectively. The superior performance of the proposed technique highlights its effectiveness in this domain.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The implementation of such automated methods can significantly improve the efficiency and accuracy of chronic disease diagnosis, benefiting both healthcare providers and patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jep.70126","type":"journal-article","created":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T03:46:12Z","timestamp":1748231172000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Optimised Hybrid Attention\u2010Based Capsule Network Integrated Three\u2010Pathway Network for Chronic Disease Detection in Retinal Images"],"prefix":"10.1111","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9059-3430","authenticated-orcid":false,"given":"M.","family":"Mohamed Yaseen","sequence":"first","affiliation":[{"name":"KCG College of Technology Chennai Tamil Nadu India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8554-6656","authenticated-orcid":false,"given":"Thulasi Bai","family":"Vijayan","sequence":"additional","affiliation":[{"name":"KCG College of Technology Chennai Tamil Nadu India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,5,26]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/data6020014"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3094649"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.preteyeres.2020.100938"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00592-020-01621-6"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2010.2084567"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-022-03490-8"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.health.2023.100140"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21165283"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.3390\/sym13040670"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.3390\/s22124310"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12061390"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.3390\/s23125393"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2020.3011805"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104163"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1080\/03772063.2024.2367046"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-021-09946-2"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-819740-0.00002-4"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICTS.2019.8850940"},{"key":"e_1_2_9_20_1","first-page":"241","article-title":"Automated Glaucoma Type Identification Using Machine Learning or Deep Learning Techniques","volume":"50","author":"Singh L. 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