The classification of medicine image by using clustering algorithm

Today, in light of innovations carried out in the field of medicine and medical imaging and processing of medical images has become more important. RNA virus identified so far, these viruses produced in cell culture, using electron microscopy, the decision is made by eye. Identification of these creatures in a lab environment only on the basis of expert knowledge and experience takes time and done so much for the detailed information is required. Also recognized in this way the whole process of microbial species from experts or from the lab environment consists of some wrong. To minimize of this error magrin, the it is seperately used Multi-entropy-Artificial Neural Networks, Multi-entropy-Adaptive Network Based Fuzzy Inference System and Multi-entropy-Fuzzy C-Means for classification of obtained RNA virus pictures. Then, rotating and scaling for each image be made during pre-treatment centers - the edges using the method of variation of distance vector images were obtained. In feature extraction and classification phases, the norm, respectively, of the logarithmic energy and entropy threshold entropy value was calculated to be 3each of the images. Thus, feature vector is obtained and this feature vector are given to ANN, ANFIS classifiers and FCM clustering inputs in classification stage. Finally, in the testing phase, the ANN, ANFIS classifiers and FCM clustering of the correct classification performance and the success rate is calculated.Key Words: RNA Virus Images, Center - Edge Exchange Method, Entropy, ANN, ANFIS, FCM, Classification, Clustering.