EEG Signal Classification Features Analysis For Brain Diseases Diagnosis

The project proposes Associate in nursing automatic network for stage classification victimization artificial neural network for tumour and brain disorder detection for medical application. The detection of the tumor could be a difficult drawback, as a result of the structure of the tumor cells. The artificially created neural network are accustomed classify the stage of brain graphical record signal that's tumor case or brain disorder case or traditional. The manual analysis of the signal is time intense, inexact and needs intensive trained person to avoid diagnostic errors. Back Propagation Network with image and processing techniques was used to implement an automatic tumor classification. The higher process was performed in 2 stages: feature extraction victimization Principal element Analysis and therefore the classification victimization Back Propagation Network (BPN). The performance of the BPN classifier was evaluated in terms of coaching performance and classification accuracies. Back Propagation Network offers quick and correct classification than alternative neural networks and it's a promising tool for classification of the Tumors.