Detection of Visual Impairment From Retinal Fundus Images with Deep Learning

Simple item page
dc.contributor.authorOlcer, Didem
dc.contributor.authorErdas, Cagatay Berke
dc.date.accessioned2023-09-21T09:34:51Z
dc.date.available2023-09-21T09:34:51Z
dc.date.issued2022
dc.description.sponsorshipNowadays, there are challenges in terms of eye care, including the treatment of visual impairment, quality of prevention and vision rehabilitation services. Since the eye is an organ that gives information about other diseases due to its structure, examinations have an important role. In this study, a solution is sought with AlexNet, ResNet and Xception architectures based on deep learning to predict the presence or absence of visual impairment from retinal fundus images. Thus, the possibility of vision loss of patients can be reduced by detecting people with visual impairments and by early diagnosis, and thus, significant increases in the patient's quality of life can be observed.en_US
dc.identifier.isbn978-1-6654-5432-2en_US
dc.identifier.scopus2-s2.0-85144069771en_US
dc.identifier.urihttp://hdl.handle.net/11727/10728
dc.identifier.wos000903709700085en_US
dc.language.isoturen_US
dc.relation.isversionof10.1109/TIPTEKNO56568.2022.9960232en_US
dc.relation.journal2022 MEDICAL TECHNOLOGIES CONGRESS (TIPTEKNO'22)en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectVisual impairment, Retina fundus, Deep learningen_US
dc.subjectAlexNeten_US
dc.subjectResNeten_US
dc.subjectXceptionen_US
dc.subjectMajority votingen_US
dc.subjectClassificationen_US
dc.titleDetection of Visual Impairment From Retinal Fundus Images with Deep Learningen_US
dc.typeconferenceObjecten_US

Files

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Mühendislik Fakültesi / Faculty of Engineering