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Ergin, Semih
Prof. Dr. Ismail SARITAS
Computer Science & IT

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The Assessment of Time-Domain Features for Detecting Symptoms of Diabetic Retinopathy Elibol, Gülin; Ergin, Semih
International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.270351

Abstract

 Diabetes affects the capillary vessels in retina and causes vision loss. This disorder of retina due to diabetes is named as Diabetic Retinopathy (DR). Diagnosing the stages of DR is performed on a publicly available database (DiaraetDB1) via detecting the symptoms of this disease. Time-domain features are extracted and selected to classify a fundus image. Fisher’s Linear Discriminant Analysis (FLDA), Linear Bayes Normal Classifier (LDC), Decision Tree (DT) and k-Nearest Neighbor (k-NN) are used as the classification methods in the experimental benchmarking. The recognition accuracies are obtained using all features (68 features) and selected features separately. k-NN is observed as the best classification method for without feature selection case and it gives averagely 92.22% accuracy. For feature selection case, LDC gives the best average accuracy as 92.45% with maximum 7 carefully chosen features.
A Genuine GLCM-based Feature Extraction for Breast Tissue Classification on Mammograms Ergin, Semih; Esener, İdil Işıklı; Yüksel, Tolga
International Journal of Intelligent Systems and Applications in Engineering 2016: Special Issue
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.269453

Abstract

A breast tissue type detection system is designed, and verified on a publicly available mammogram dataset constructed by the Mammographic Image Analysis Society (MIAS) in this paper. This database consists of three fundamental breast tissue types that are fatty, fatty-glandular, and dense-glandular. At the pre-processing stage of the designed detection system, median filtering and morphological operations are applied for noise reduction and artifact suppression, respectively; then a pectoral muscle removal operation follows by using a region growing algorithm. Then, 88-dimensional texture features are computed from the GLCMs (Gray-Level Co-Occurrence Matrices) of mammogram images. Besides, a formerly introduced 108-dimensional feature ensemble is also computed and cascaded with the 88-dimensional texture features. Finally, a classification process is realized using Fisher’s Linear Discriminant Analysis (FLDA) classifier in four different classification cases: one-stage classification, first fatty – then others, first fatty-glandular – then others, and first dense-glandular – then others. A maximum of 72.93% classification accuracy is achieved using only texture features whereas it is increased to 82.48% when cascade features are utilized. This consequence clearly exposes that the cascade features are more representative than texture features. The maximum classification accuracy is attained when “first fatty-glandular – then others” classification case is implemented, that is consistent with the fact that fatty-glandular tissue type is easily confused with fatty and dense-glandular tissue types.
Co-Authors Elibol, Gülin Esener, İdil Işıklı Yüksel, Tolga