Retracted: Multiclass classification of kirlian images using svm technique

Every organism emits energy around it which comprises UV-radiation, EM-radiation, infrared and thermal radiation. This energy around human body represents health condition of the subject under study. These energy fields are called as aura of the body under consideration. Several types of equipments are there to capture such energy. Kirlian camera captures the distribution of energy radiation around each finger of both hands and maps them into energy distribution various organs of our body. In this work classification of human diseases based on Kirlian image features is done. Few Kirlian images in each of the 6 different categories of diseased organs of the body are taken and classified based on defected region of the body. The auras are extracted from images using morphological pre-processing, segmentation, shape and texture features extraction followed by training and classification of them using machine learning methods like Multiclass-SVM and ANN. Greater average accuracy of classification is achieved with MSVM-technique. Overall classification accuracy of 13.688% and 80.0655% are achieved in ANN and MSVM-techniques respectively.