期刊名称：International Journal of Computer Science and Network Solutions
印刷版ISSN：2345-3397
出版年度：2014
卷号：2
期号：8
页码：31-38
出版社：International Journal of Computer Science and Network Solutions
摘要：Nowadays, Magnetic Resonance Images (MRI) is the most common tool for diagnosis of soft tissues.Using fully automated classification magnetic resonance images of the human brain that are important forclinical research studies, can be detect the healthy or sick person. This paper purpose enhances theclassification accuracy and achieves good performance in classifying the MRI images. Automatic classificationof images uses the models and formal criteria involving key stages of feature extraction, feature reduction andlearning algorithms. In this study, the best-known and most effective feature extraction algorithms, reducingthe features, such as wavelet transform principal component analysis. Also a hybrid approach to improve theefficiency of the support vector classifier using Cuckoo evolutionary algorithm has been employed. While theinitial values in support vector algorithm are set manually, the values in proposed algorithm retrieved fromCuckoo algorithm automatically. The proposed method is evaluated using these criteria: accuracy, sensitivityand specificity. The results of the combined classification accuracy, sensitivity and specificity are above 98%that stronger and more effective compared with other recent work.
