{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T13:48:07Z","timestamp":1782481687168,"version":"3.54.5"},"reference-count":44,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,29]],"date-time":"2023-01-29T00:00:00Z","timestamp":1674950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Automatic screening of diabetic retinopathy (DR) is a well-identified area of research in the domain of computer vision. It is challenging due to structural complexity and a marginal contrast difference between the retinal vessels and the background of the fundus image. As bright lesions are prominent in the green channel, we applied contrast-limited adaptive histogram equalization (CLAHE) on the green channel for image enhancement. This work proposes a novel diabetic retinopathy screening technique using an asymmetric deep learning feature. The asymmetric deep learning features are extracted using U-Net for segmentation of the optic disc and blood vessels. Then a convolutional neural network (CNN) with a support vector machine (SVM) is used for the DR lesions classification. The lesions are classified into four classes, i.e., normal, microaneurysms, hemorrhages, and exudates. The proposed method is tested with two publicly available retinal image datasets, i.e., APTOS and MESSIDOR. The accuracy achieved for non-diabetic retinopathy detection is 98.6% and 91.9% for the APTOS and MESSIDOR datasets, respectively. The accuracies of exudate detection for these two datasets are 96.9% and 98.3%, respectively. The accuracy of the DR screening system is improved due to the precise retinal image segmentation.<\/jats:p>","DOI":"10.3390\/bdcc7010025","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T04:58:25Z","timestamp":1675054705000},"page":"25","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":102,"title":["A Novel Approach for Diabetic Retinopathy Screening Using Asymmetric Deep Learning Features"],"prefix":"10.3390","volume":"7","author":[{"given":"Pradeep Kumar","family":"Jena","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, NIST Institute of Science and Technology, Berhampur, Odisha 761008, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6223-0413","authenticated-orcid":false,"given":"Bonomali","family":"Khuntia","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Berhampur University, Brahmapur, Odisha 760007, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charulata","family":"Palai","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, NIST Institute of Science and Technology, Berhampur, Odisha 761008, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6383-780X","authenticated-orcid":false,"given":"Manjushree","family":"Nayak","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, NIST Institute of Science and Technology, Berhampur, Odisha 761008, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6363-5017","authenticated-orcid":false,"given":"Tapas Kumar","family":"Mishra","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, SRM University-AP, Amaravati, Andhra Pradesh 522240, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4939-0797","authenticated-orcid":false,"given":"Sachi Nandan","family":"Mohanty","sequence":"additional","affiliation":[{"name":"School of Computer Science & Engineering (SCOPE),VIT-AP University, Amaravati, Andhra Pradesh 522508, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107810","DOI":"10.1016\/j.patcog.2020.107810","article-title":"Automated segmentation of the optic disc from fundus images using an asymmetric deep learning network","volume":"112","author":"Wang","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1007\/s00521-015-1929-5","article-title":"Automatic detection of microaneurysms in colour fundus images for diabetic retinopathy screening","volume":"27","author":"Rahim","year":"2015","journal-title":"Neural Comput. 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