{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:19:46Z","timestamp":1753881586358,"version":"3.41.2"},"reference-count":0,"publisher":"World Scientific Pub Co Pte Ltd","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2023,2]]},"abstract":"<jats:p> Diabetic retinopathy (DR), a common complication of diabetes, is one of the leading causes of visual loss in a growing population. Thus, it is essential to identify DR at an early stage in order to minimize the problem of vision loss. As a result, the Henry Gas SailFish Optimizer (HGSO) algorithm and a successful lesion segmentation method based on Entropy Weighted and Kernalized Power K-Means Clustering (EWKPC) are developed for the DR detection. However, the SailFish Optimizer (SFO) and Henry Gas Solubility Optimization (HGSO) were combined to form the novel approach known as HGSO. Here, noise from the image is removed during pre-processing using the Region of Interest (ROI) approach and median filtering. Another use of the suggested EWKPC approach is lesion segmentation. Based on the outcomes of the data augmentation phase, Deep Convolutional Neural Network (DCNN)-based DR detection is carried out using the created HGSO algorithm. Along with effective performance, the created HGSO-based DCNN also displayed higher accuracy (0.910), specificity (0.929), and sensitivity (0.909). <\/jats:p>","DOI":"10.1142\/s0218213022500440","type":"journal-article","created":{"date-parts":[[2022,9,25]],"date-time":"2022-09-25T11:04:30Z","timestamp":1664103870000},"source":"Crossref","is-referenced-by-count":2,"title":["Entropy Weighted and Kernalized Power K-Means Clustering Based Lesion Segmentation and Optimized Deep Learning for Diabetic Retinopathy Detection"],"prefix":"10.1142","volume":"32","author":[{"given":"J. Granty Regina","family":"Elwin","sequence":"first","affiliation":[{"name":"Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore, India"}]},{"given":"K. Suresh","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Saveetha Engineering College, Chennai, India"}]},{"given":"J. P.","family":"Ananth","sequence":"additional","affiliation":[{"name":"Department of CSE, Sri Krishna College of Engineering and Technology, Coimbatore, India"}]},{"given":"R. Ramesh","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Sri Krishna College of Technology, Coimbatore, India"}]}],"member":"219","published-online":{"date-parts":[[2023,2,28]]},"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218213022500440","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T10:10:39Z","timestamp":1677665439000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S0218213022500440"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2]]},"references-count":0,"journal-issue":{"issue":"01","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["10.1142\/S0218213022500440"],"URL":"https:\/\/doi.org\/10.1142\/s0218213022500440","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"type":"print","value":"0218-2130"},{"type":"electronic","value":"1793-6349"}],"subject":[],"published":{"date-parts":[[2023,2]]},"article-number":"2250044"}}