抽象的

Brain Tumor Classification Using PCA & PNN

Mr.M.Arun, Mr. Ashok Kumar, N.P.Ria, H.Sandhya Bhargavi

The conventional method of detection and classification of brain tumor is by human inspection with the use of medical resonant brain images. But it is impractical when large amounts of data is to be diagnosed and to be reproducible. And also the operatorassisted classification leads to false predictions and may also lead to false diagnose. Medical Resonance images contain a noise caused by operator performance which can lead to serious inaccuracies classification. The use of artificial intelligent techniques for instant, neural networks, and fuzzy logic shown great potential in this field. Probabilistic Neural Network gives fast and accurate classification and is a best tool for classification of the tumors. Probabilistic Neural Network with image and data processing techniques is implemented for automated brain tumor classification. Decision making was performed in two stages: feature extraction using the principal component analysis and the Probabilistic Neural Network (PNN). The performance of the PNN classifier was evaluated in terms of training performance and classification accuracies. Probabilistic Neural Network gives fast and accurate classification and is a promising tool for classification of the tumors