An expert diagnosis system for classification of human parasite eggs based on multi-class SVM |
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| Authors: | Derya Avci Asaf Varol |
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| Affiliation: | 1. Department of Informatics, Firat University, Elazig, Turkey;2. Department of Computer Engineering, Kahramanmaras Sutcu Imam University, Kahramanmaras, Turkey;3. Vocational School of Technical Sciences, Firat University, Elazig, Turkey;1. Department of Parasitology, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia;;2. Department of Microbiology, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore;;3. Singapore Immunology Network (SIgN), A*STAR, Singapore;;4. Instituto de Biologia, Universidade Estadual de Campinas, São Paulo, Brazil;;5. School of Biological Sciences, Nanyang Technological University, Singapore;;6. Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand; and;7. Centre for Tropical Medicine, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom;1. First Department of Pediatric Surgery, Genimatas General Hospital, Aristotle University of Thessaloniki Medical School, Thessaloniki, Greece;2. Department of Pathology, Genimatas General Hospital, Thessaloniki, Greece |
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| Abstract: | In this paper, it is proposed a new methodology based on invariant moments and multi-class support vector machine (MCSVM) for classification of human parasite eggs in microscopic images. The MCSVM is one of the most used classifiers but it has not used for classification of human parasite eggs to date. This method composes four stages. These are pre-processing stage, feature extraction stage, classification stage, and testing stage. In pre-processing stage, the digital image processing methods, which are noise reduction, contrast enhancement, thresholding, and morphological and logical processes. In feature extraction stage, the invariant moments of pre-processed parasite images are calculated. Finally, in classification stage, the multi-class support vector machine (MCSVM) classifier is used for classification of features extracted feature extraction stage. We used MATLAB software for estimating the success classification rate of proposed approach in this study. For this aim, proposed approach was tested by using test data. At end of test, 97.70% overall success rates were obtained. |
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