An expert diagnosis system for classification of human parasite eggs based on multi-class SVM - Avci, Derya; Varol, Asaf (Pergamon-Elsevier Science Ltd, 2009)
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. (C) 2007 Elsevier Ltd. All rights reserved.
Keywords: Parasites; Parasite eggs; Microscopic image; Multi-class support vector machines; Expert systems