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Öğe Assessing the importance of features for detection of hard exudates in retinal images(Tubitak Scientific & Technological Research Council Turkey, 2017) Akyol, Kemal; Sen, Baha; Bayir, Safak; Cakmak, Hasan BasriDiabetes 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.Öğe A Novel Classification and Estimation Approach for Detecting Keratoconus Disease with Intelligent Systems(Ieee, 2013) Ucar, Murat; Sen, Baha; Cakmak, Hasan BasriKeratoconus is an eye disease characterized by progressive thinning of cornea which is the front based transparent layer of the eye. In other words, it is a progressive distortion of corneal layer and at least getting conical shape that should be like a dome camber. The vision reduces more and more while cornea gets shape of cone which should be like a sphere normally. The aim of this study is to define a new classification method for detecting keratoconus based on statistical analysis and to realize the prediction of these classified data with intelligent systems. 301 eyes of 159 patients and 394 eyes of 265 refractive surgery candidates as the control group have been used for this study. Factor analysis, one of the multivariate statistical techniques, has been mainly used to find more meaningful, easy to understand, and independent factors amongst the others. Later, a new classification method has been defined using clustering analysis techniques on these factors and finally estimated by using artificial neural networks and support vector machines.Öğe A statistical approach to classification of keratoconus(Ijo Press, 2016) Ucar, Murat; Cakmak, Hasan Basri; Sen, Baha[No abstract available]
