Browsing by Author "Cigdem, Ozkan"

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Citation - Scopus: 7
Classification of Healthy Siblings of Bipolar Disorder Patients From Healthy Controls Using Mri
(IEEE, 2019) Cigdem, Ozkan; Soyak, Refik; Aydeniz, Burhan; Oguz, Kaya; Demirel, Hasan; Kitis, Omer; Eker, Cagdas
Three Dimensional magnetic resonance imaging (3D-MRI) has been utilized to classify patients with neuroanatomical abnormalities apart from healthy controls (HCs). The studies on the diagnosis of Bipolar Disorder (BD) focuses also on the unaffected relatives of BD patients in order to examine the heritable resistance factors associated with the disorder. Hence, the comparison of Healthy Siblings of Bipolar Disorder patients (HSBDs) and HCs is also required owing to the high heritability of BD. In this paper, the classification of 27HSBDs from 38HCs has been studied by using 3D-MRI and Computer-Aided Detection (CAD). The pre-processing of 3D-MRI data is performed by taking advantage of Voxel-Based Morphometry (VBM) and the structural deformations in the Gray Matter (GM) and White Matter (WM) are obtained by using a general linear model. The model is configured by using a two sample t-test technique and Total Intracranial Volume (TIV) as a covariate. The altered voxels between data groups are considered as Voxel of Interests (VOIs) and the 3D masks are generated for GM and WM tissue probability maps. The Relief-F algorithm is utilized to rank the features and a Fisher Criterion (FC) method is considered to determine the number of top-ranked discriminative features. The performances of Support Vector Machines (SVM) and the Naive Bayes (NB) algorithms are compared on the classification of HSBD and HC. The experiments are performed for GM-only, WM-only, and their combinations. The experimental results indicate that the changes between the brain regions of HSBD and HC might provide information on the heritable factors associated with the BD. Additionally, it is concluded that using the combination of GM and WM tissue probability map provides better results than considering them, separately. Finally, it is obtained that the classification accuracy of SVM on HSBD and HC comparison is better than that of NB.
Citation - Scopus: 3
Diagnosis of Bipolar Disease Using Correlation-Based Feature Selection With Different Classification Methods
(IEEE, 2019) Cigdem, Ozkan; Sulucay, Aysu; Yilmaz, Arif; Oguz, Kaya; Demirel, Hasan; Kitis, Omer; Eker, Cagdas
Three-Dimensional Magnetic Resonance Imaging (3D-MRI) and Computer-Aided Detection (CAD) have been widely studied in the detection of bipolar disorder (BD). In this study, the structural alterations at the grey matter (GM) and white matter (WM) of BD subjects versus healthy controls (HCs) have been compared using Voxel-Based Morphometry (VBM). In order to obtain 3D GM and WM masks, the two sample t-test method and total intracranial volumes of BD and HC as a covariate have been utilized. In addition to analyzing effects of GM and WM tissue maps separately in the detection of BD, impacts of both GM and WM ones are studied by concatenating them in a matrix. The correlation-based feature selection (CFS) feature ranking method is applied to the obtained 3D masks to rank the features, the number of selected top-ranked features are determined using a Fisher criterion (FC) approach, and different classification algorithms are used to classify BD apart from HCs. In this study, 26 BDs and 38 HCs data are used. The experimental results indicate that the classification accuracy of Naive Bayes outperforms the other four classification algorithms used in this study. Additionally, concatenation of GM and WM tissue maps enhances the classification performances of using GM-only and WM-only ones. The classification accuracies obtained for GM, WM, and their concatenation are 72.92%, 78.33%, and 80.00% respectively.
Citation - WoS: 1
Citation - Scopus: 3
Effects of Covariates on Classification of Bipolar Disorder Using Structural Mri
(IEEE, 2019) Cigdem, Ozkan; Horuz, Erencan; Soyak, Refik; Aydeniz, Burhan; Sulucay, Aysu; Oguz, Kaya; Demirel, Hasan
Three-Dimensional Magnetic Resonance Imaging (3D-MRI) and Computer-Aided Detection (CAD) have been widely studied in the detection and diagnosis of neuroanatomical abnormalities, including bipolar disorder (BD). Pre-processing of 3D-MRI scans plays an important role in post-processing. In this study, Voxel-Based Morphometry (VBM) is used to compare the morphological differences at the grey matter (GM) and white matter (WM) of BD subjects versus healthy controls (HCs). The effects of using different covariates (i.e. total intracranial volume (TIV), age, sex, and their combinations) on classification of BDs from HCs have been investigated for GM-only, WM-only, and their combination. 3D masks for GM and WM are generated separately by using local differences between BPs and HCs and the two sample t-test method. Principle component analysis based dimensionality reduction and support vector machine with Gaussian kernel are employed for classification of 26 BDs and 38 HCs obtained from Ege University, School of Medicine, Department of Psychiatry. The results indicate that using only TIV as a covariate provides more robust results for BD classification compared to other covariate combinations. Furthermore, the combination of GM and WM improves classification performance. The highest classification accuracies obtained for GM, WM, and their combination are 70.30%, 79.70%, and 82.80% respectively.
Citation - WoS: 9
Citation - Scopus: 15
The Performance of Local-Learning Based Clustering Feature Selection Method on the Diagnosis of Parkinson's Disease Using Structural Mri
(IEEE, 2019) Cigdem, Ozkan; Demirel, Hasan; Unay, Devrim
The neurodegenerative diseases are modelled by the deformation of the brain neurons. In the detection of neurodegenerative diseases including Parkinson's Disease (PD), the Three-Dimensional Magnetic Resonance Imaging (3D-MRI) has been utilized, recently. In this paper, by using a Voxel-Based Morphometry (VBM) method, the morphological alterations between the Structural MRI (sMRI) data of 40PD and 40 Healthy Controls (HCs) have been determined. By using the structural alterations between the PD patients and HC and two sample t-test method, the 3D Gray Matter (GM) and White Matter (WM) tissue masks are obtained separately for two different hypotheses, t-contrast and f-contrast. The Feature Selection and Kernel Learning for Local Learning-based Clustering (LLCFS) method is used to rank the features and a Fisher criterion algorithm is utilized to determine the number of the topranked features. The selected features are classified by using two classification approaches, namely Support Vector Machines (SVM) and Naive Bayes (NB). The results indicate that the classification performances of both NB and SVM methods with f-contrast outperform that with t-contrast for all GM, WM, and the concatenation of GM and WM tissue volumes. Additionally, the classification performance of SVM is higher than that of NB for all GM, WM, and the combination of GM and WM tissues. The highest area under curve results are obtained as 75.63%, 85.00%, and 90.00% for GM, WM, and the concatenation of them, respectively.