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@article{1649758, author = {Kůs, Radomír and Schwarz, Daniel}, article_location = {BUDAPEST}, article_number = {5}, doi = {http://dx.doi.org/10.12700/APH.14.5.2017.5.12}, keywords = {feature extraction; computer-aided diagnosis; schizophrenia; brain morphometry; voxel-based morphometry; deformation-based morphometry; magnetic resonance imaging; classification; machine learning}, language = {eng}, issn = {1785-8860}, journal = {Acta Polytechnica Hungarica}, title = {Computer-aided Diagnostics of Schizophrenia: Comparison of Different Feature Extraction Methods}, url = {http://acta.uni-obuda.hu/Radomir_Daniel_76.pdf}, volume = {14}, year = {2017} }
TY - JOUR ID - 1649758 AU - Kůs, Radomír - Schwarz, Daniel PY - 2017 TI - Computer-aided Diagnostics of Schizophrenia: Comparison of Different Feature Extraction Methods JF - Acta Polytechnica Hungarica VL - 14 IS - 5 SP - 181-196 EP - 181-196 PB - Budapest TECH SN - 17858860 KW - feature extraction KW - computer-aided diagnosis KW - schizophrenia KW - brain morphometry KW - voxel-based morphometry KW - deformation-based morphometry KW - magnetic resonance imaging KW - classification KW - machine learning UR - http://acta.uni-obuda.hu/Radomir_Daniel_76.pdf L2 - http://acta.uni-obuda.hu/Radomir_Daniel_76.pdf N2 - Receiving an early diagnosis of schizophrenia is a crucial step towards its treatment. However, in current thinking, the diagnosis is based on time-consuming criteria, burdened with subjectivity. Hence, objective and more reliable therapeutic tests are desirable for the clinical practice of Psychiatry. Since schizophrenia is characterized by progressive brain volume changes during the course of the disease, many studies have recently turned attention to machine learning and brain morphometric techniques serving as tools for computer-aided diagnosis of schizophrenia based on neuroimaging data. In our study, the methodology is applied to distinguish between 52 first-episode schizophrenia patients and 52 healthy volunteers on the basis of T1-weighted magnetic resonance images of their brains preprocessed by the means of voxel-based and deformation-based morphometry. The proposed classification schemes vary in the feature extraction and selection steps. Namely, Mann-Whitney testing is implemented as a simple univariate approach playing the role of a comparator to multivariate methods such as inter-subject PCA, the K-SVD algorithm, and pattern-based morphometry. The highest classification accuracy, 70%, is reached with the pattern-based morphometry technique. The study points out the difference between univariate and multivariate approaches towards neuroimaging data. Additionally, the contrast between feature extraction capabilities of voxel-based and deformation-based morphometry is demonstrated. ER -
KŮS, Radomír a Daniel SCHWARZ. Computer-aided Diagnostics of Schizophrenia: Comparison of Different Feature Extraction Methods. \textit{Acta Polytechnica Hungarica}. BUDAPEST: Budapest TECH, 2017, roč.~14, č.~5, s.~181-196. ISSN~1785-8860. Dostupné z: https://dx.doi.org/10.12700/APH.14.5.2017.5.12.
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