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

dc.contributor.authorAkyol, Kemal
dc.contributor.authorŞen, Baha
dc.contributor.authorBayır, Şafak
dc.contributor.authorÇakmak, Hasan Basri
dc.date.accessioned2024-09-29T16:33:47Z
dc.date.available2024-09-29T16:33:47Z
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 classi er (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 classi er algorithms\\' performances by using 5-fold cross validation on these important features\\' dataset and it was observed that the random forest (RF) classi er is the best classi er. 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 classi er.en_US
dc.identifier.endpage1237en_US
dc.identifier.issn1300-0632
dc.identifier.issue2en_US
dc.identifier.startpage1223en_US
dc.identifier.trdizinid248532en_US
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/248532
dc.identifier.urihttps://hdl.handle.net/20.500.14619/12176
dc.identifier.volume25en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciencesen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMühendisliken_US
dc.subjectElektrik ve Elektroniken_US
dc.titleAssessing the importance of features for detection of hard exudates in retinal imagesen_US
dc.typeArticleen_US

Dosyalar