Assessing the importance of features for detection of hard exudates in retinal images

Basit Öğe Kaydı
dc.authoridCAKMAK, HASAN BASRI/0000-0001-6877-8773
dc.authoridSEN, BAHA/0000-0003-3577-2548
dc.authoridAKYOL, KEMAL/0000-0002-2272-5243
dc.authoridBAYIR, Safak/0000-0003-4719-8088
dc.contributor.authorAkyol, Kemal
dc.contributor.authorSen, Baha
dc.contributor.authorBayir, Safak
dc.contributor.authorCakmak, Hasan Basri
dc.date.accessioned2024-09-29T16:08:19Z
dc.date.available2024-09-29T16:08:19Z
dc.date.issued2017
dc.departmentKarabük Üniversitesien_US
dc.description.abstractDiabetes disrupts the operation of the eye and leads to vision loss, affecting particularly the nerve layer and capillary vessels in this layer by changes in the blood vessels of the retina. Suddenly loss and blurred vision problems occur in the image, depending on the phase of the disease, called diabetic retinopathy. Hard exudates are one of the primary signs of diabetic retinopathy. Automatic recognition of hard exudates in retinal images can contribute to detection of the disease. We present an automatic screening system for the detection of hard exudates. This system consists of two main steps. Firstly, the features were extracted from patch images consisting of hard exudate and normal regions using the DAISY algorithm based on the histogram of oriented gradients. After, we utilized the recursive feature elimination (RFE) method, using logistic regression (LR) and support vector classifier (SVC) estimators on the raw dataset. Therefore, we obtained two datasets containing the most important features. The number of important features in each dataset created with LR and SVC was 126 and 259, respectively. Afterward, we observed different classifier algorithms' performances by using 5-fold cross validation on these important features' dataset and it was observed that the random forest (RF) classifier is the best classifier. Secondly, we obtained important features from the feature vector that corresponds with the region of interest in accordance with the keypoint information in a new retinal fundus image. Then we performed detection of hard exudate regions on the retinal fundus image by using the RF classifier.en_US
dc.identifier.doi10.3906/elk-1508-71
dc.identifier.endpage1237en_US
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85017334509en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage1223en_US
dc.identifier.urihttps://doi.org/10.3906/elk-1508-71
dc.identifier.urihttps://hdl.handle.net/20.500.14619/7490
dc.identifier.volume25en_US
dc.identifier.wosWOS:000399461300045en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTubitak Scientific & Technological Research Council Turkeyen_US
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectComputer-aided analysisen_US
dc.subjectcomputer visionen_US
dc.subjectfeature extractionen_US
dc.subjectimportant featuresen_US
dc.subjectimage recognitionen_US
dc.titleAssessing the importance of features for detection of hard exudates in retinal imagesen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu