نمایش مختصر رکورد

dc.contributor.authorNurrahmadayeni, Nen_US
dc.contributor.authorEfendi, Syahrilen_US
dc.contributor.authorZarlis, Muhammaden_US
dc.date.accessioned1400-12-14T14:22:47Zfa_IR
dc.date.accessioned2022-03-05T14:22:47Z
dc.date.available1400-12-14T14:22:47Zfa_IR
dc.date.available2022-03-05T14:22:47Z
dc.date.issued2022-01-01en_US
dc.date.issued1400-10-11fa_IR
dc.date.submitted2021-11-10en_US
dc.date.submitted1400-08-19fa_IR
dc.identifier.citationNurrahmadayeni, N, Efendi, Syahril, Zarlis, Muhammad. (2022). Analysis of deep learning methods in diabetic retinopathy disease identification based on retinal fundus image. International Journal of Nonlinear Analysis and Applications, 13(1), 1639-1647. doi: 10.22075/ijnaa.2022.5779en_US
dc.identifier.issn2008-6822
dc.identifier.urihttps://dx.doi.org/10.22075/ijnaa.2022.5779
dc.identifier.urihttps://ijnaa.semnan.ac.ir/article_5779.html
dc.identifier.urihttps://iranjournals.nlai.ir/handle/123456789/879718
dc.description.abstractDiabetic retinopathy (DR) is a serious retinal disease and is considered the leading cause of blindness and is strongly associated with people with diabetes. Ophthalmologists use optical coherence tomography (OCT) and retinal fundus imagery to assess the retinal thickness, structure, and also detecting edema, bleeding, and scarring. Deep learning models are used to analyze OCT or fundus images, extract unique features for each stage of DR, then identify images and determine the stage of the disease. Our research using retinal fundus imagery is used to identify diabetic retinopathy disease, among others, using the Convolutional Neural Network (CNN) method. The methodology stage in the study was a green channel, Contrast Limited Adaptive Histogram Equalization (CLAHE), morphological close, and background exclusion. Next, a segmentation process is carried out that aims to generate binary imagery using thresholding techniques. Then the binary image is used as training data conducted epoch as much as 30 times to obtain an optimal training model. After testing, the deep learning method with the CNN algorithm obtained 95.355\% accuracy in the identification of diabetic retinopathy disease based on fundus image in the retina.en_US
dc.format.extent550
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoen_US
dc.publisherSemnan Universityen_US
dc.relation.ispartofInternational Journal of Nonlinear Analysis and Applicationsen_US
dc.relation.isversionofhttps://dx.doi.org/10.22075/ijnaa.2022.5779
dc.subjectDeep Learning Methodsen_US
dc.subjectDiabetic Retinopathyen_US
dc.subjectRetinal Fundus Imageen_US
dc.titleAnalysis of deep learning methods in diabetic retinopathy disease identification based on retinal fundus imageen_US
dc.typeTexten_US
dc.typeResearch Paperen_US
dc.contributor.departmentDepartment of Master in Informatic Engineering, Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Medan, Indonesiaen_US
dc.contributor.departmentDepartment of Master in Informatic Engineering, Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Medan, Indonesiaen_US
dc.contributor.departmentDepartment of Master in Informatic Engineering, Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Medan, Indonesiaen_US
dc.citation.volume13
dc.citation.issue1
dc.citation.spage1639
dc.citation.epage1647


فایل‌های این مورد

Thumbnail

این مورد در مجموعه‌های زیر وجود دارد:

نمایش مختصر رکورد