A Technique to Tumor Detection from Brain MRI Images Using FCM and Neuro-Fuzzy Classifier

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Segmentation of medical imagery is a challenging problem due to the complexity of the images, as well as to the absence of models of the anatomy that fully capture the possible deformations in each structure. Image segmentation is an indispensable part of the tumour identification, particularly during analysis of Magnetic Resonance (MR) images. Recently, plenty of techniques are available in the literature for detection of brain tumor using MRI images.  Most of the works make use of different machine learning techniques to provide the detection accuracy in a more effective way. In our proposed method, we include following major steps, i) Pre-processing, ii) Segmentation, iii) feature extraction, iv Tumor classification. At first, the input image is given to the pre-processing step to make suitable for further image processing steps. Then, the segmentation will be carried out using the fuzzy c-means clustering so that the feature can be computed from the segments itself. Subsequently, the feature extraction methods such as, shape and texture are used to find the features for classification. Finally, the neuro-fuzzy classifier is used to find whether the input image is tumor image or not. The Comparative analysis is carried out with Radial Basis Function (RBF) neural network, Neuro fuzzy and the Feed Forward Neural Network (FFNN) and the obtained results are analysed in terms of sensitivity, specificity and accuracy.
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Brain MRI image; Tumor; Fuzzy C-Means Algorithm; Feature Extraction; NLGXP; Neuro Fuzzy Classfier; RBF; FFNN

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