Brain Tumor Classification Using PNN And Clustering | Abstract

ISSN ONLINE(2319-8753)PRINT(2347-6710)

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Brain Tumor Classification Using PNN And Clustering


Probabilistic Neural Network (PNN) also termed to be a learning machine is preliminarily used with an extension of various image classifications based on Training networks and Testing networks. To efficiently detect Brain Tumor cells, clustering method based on FCM can also be implemented. The Probabilistic Neural Network (PNN) will be employed to classify the various stages of Tumor cut levels such as Benign, Malignant or Normal. Probabilistic Neural Network with Radial Basis Function will be applied to implement tumor cells segmentation and classification. Decision should be made to classify the input image as normal or abnormal cells. This can be performed in two stages: Gray-Level Cooccurrence Matrix and the classification using Neural Network based function. The schematic method for Computerized Tomography based tumor cells detection is done using human inspection method. Probabilistic Neural Network with Discrete Cosine Transform has been imparted for Brain Tumor Classification. Prediction of malignant cells or non-tumor cells can be executed using two variants: i) Feature extraction using the Discrete Cosine Transform and ii) classification using Probabilistic Neural Network (PNN).


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