TAXONOMY OF BRAIN TUMOR CLASSIFICATION TECHNIQUES: A SYSTEMATIC REVIEW

The use of digital image processing has become very demanding in various areas including medical applications. There are many applications where image processing is used to understand, analyze, interpret and make decisions. The main purpose of image processing is to improve the quality of the images for human/machine perception. The image processing techniques implemented for the detection of tumor from MRI images consist of image pre-processing, segmentation, feature extraction and classification steps. In this paper we have analyzed existing brain tumor detection and classification techniques. Brain image classification is very important because it provides anatomical structure information, which is necessary for planning of the treatment and patient follow-up. Thus various methods are surveyed in order to get better classification accuracy in terms of specificity, sensitivity and accuracy. This survey serves to classify the brain MRI images into normal, benign and malignant tumor. Classification of tumor is done with various techniques like Artificial Neural Networks (ANN), Deep Neural Networks (DNN), K- Nearest Neighbor (KNN), Support Vector Machine (SVM), Sequential Minimal Optimization (SMO) etc