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The Automatic Support Intelligent System is used to detect Brain Tumor through the combination of neural network and fuzzy logic system. It helps in the diagnostic and aid in the treatment of the brain tumor. The detection of the Brain Tumor is a challenging problem, due to the structure of the Tumor cells in the brain. This project presents an analytical method that enhances the detection of brain tumor cells in its early stages and to analyze anatomical structures by training and classification of the samples in neural network system and tumor cell segmentation of the sample using fuzzy clustering algorithm. The artificial neural network will be used to train and classify the stage of Brain Tumor that would be benign, malignant or normal. The Fast discrete curvelet transformation is used to analysis texture of an image. In brain structure analysis, the tissues which are WM and GM are extracted. Probabilistic Neural Network with radial basis function is employed to implement an automated Brain Tumor classification. Decision making is performed in two stages: feature extraction using GLCM and the classification using PNN-RBF network. The segmentation is performed by fuzzy logic system and its result would be used as a base for early detection of Brain Tumor which would improves the chances of survival for the patient. The performance of this automated intelligent system evaluates in terms of training performance and classification accuracies to provide the precise and accurate results. The simulated result enhances and shows that classifier and segmentation algorithm provides better accuracy than previous methodologies.
How to Cite
, A. G. P. C. M. M. (2018). Revista ew on Brain Tumour Detection using Image Processing. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 4(1), 428–430. Retrieved from https://ijfrcsce.org/index.php/ijfrcsce/article/view/1035