Towards an autonomous human chromosome classification system using Competitive Support Vector Machines Teams (CSVMT)

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dc.authorid0000-0002-8098-3611en_US
dc.contributor.authorKuşakcı, Ali Osman
dc.contributor.authorAyvaz, Berk
dc.contributor.authorKarakaya, Elif
dc.date.accessioned2019-08-06T12:44:50Z
dc.date.available2019-08-06T12:44:50Z
dc.date.issued2017en_US
dc.departmentFakülteler, Mühendislik ve Tasarım Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractSupport Vector Machines Karyotyping Chromosome classification Committee machinesen_US
dc.description.abstractIn broad terms, karyotyping is the process of examination and classification of human chromosome images to diagnose genetic diseases and disorders. It requires time consuming manual examination of cell images by a cytogeneticist to distinguish chromosome classes from each other. Thus, a reliable au- tonomous human chromosome classification system not only saves time and money but also reduces er- rors due to the inadequate knowledge level of the expert. Human cell contains 23 pairs of chromosome, 22 autosomes and a pair of sex chromosomes. Hence, we face a multi-class classification task which rep- resents a challenging case for any sort of classifier. In this work, to solve this classification problem, we propose a novel methodology consisting two stages: (i) data preparation and training, and (ii) testing. To determine the most informative content of the dataset several preliminary experiments are conducted and a Principal Component Analysis is done. Then, a single Support Vector Machine (SVM ij ) is trained to separate a pair of classes, (i,j) where a numerical optimization method Pattern Search (PS), is employed to find the optimal parameters for the SVM ij . Considering 22 pairs of autosomes, 22 ×22 experts are trained and optimized. The cluster of experts, we obtain is named as Competitive SVM Teams (CSVMTs) where each SVM ij competes with the others to label a new classification instance. The final output of the clas- sifier is determined by majority voteing. The results obtained on Copenhagen dataset proves the merit of the algorithm as correct classification rates (CRR) on train and test samples are 99.55% and 97.84% respectively, which are higher than any accuracy rate achieved so far in the related literature.en_US
dc.identifier.endpage234en_US
dc.identifier.scopus2-s2.0-85020161367en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage224en_US
dc.identifier.urihttps://hdl.handle.net/11467/2842
dc.identifier.volume86en_US
dc.identifier.wosWOS:000405973500020en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSupport Vector Machinesen_US
dc.subjectKaryotypingen_US
dc.subjectChromosome Classificationen_US
dc.subjectCommittee Machinesen_US
dc.titleTowards an autonomous human chromosome classification system using Competitive Support Vector Machines Teams (CSVMT)en_US
dc.typeArticleen_US

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