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Diagnosis Brain Diseases Using EEG Signal Classification Features

B. Sivaranjani, S. Rekha


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 tumour could be a difficult drawback, as a result of the structure of the tumour cells. The artificially created neural network are accustomed classify the stage of brain graphical record signal that's tumour 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 tumour 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.


Back Propagation Network, Feature Extraction, Principal Component Analysis (PCA), Electroencephalogram (EEG)

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