An Efficient Segmentation and Classification of Brain Tumour Detection Using Optimization Techniques

Abstract

Human society has great influence from various diseases where some of newlinethem are harmful. Brain tumor is the deadly disease which occurs on the glands of newlinebrain tissues. Diagnosing the presence of disease at the early stage helps the medical newlinepractitioner to provide effective treatment. In general, the presence of disease is newlineidentified through the manual intervention on the brain MRI image which introduces newlinehigher false results. Image processing techniques has great impact on the problem of newlinemedical diagnosis and healthcare solution. There are number of image processing newlinetechniques available throughout the literature which includes Support Vector newlineMachine (SVM), K-Nearest Neighbor (KNN), Particle Swarm Optimization (PSO) newlineand etc.. Each method consider different features from gray scale, variance, texture, newlineshape, edge and so on. However, the efficiency of the method is depending on the newlinekind of feature used and method of similarity measurement. Further, the above said newlinemethods of machine learning are not capable of handling huge volume of images as newlinefor any decisive support system, it needs to utilize huge volume of images which newlinesupport the achievement of higher accuracy. Deep learning algorithms like newlineConvolution neural network are capable of handling huge volume of images towards newlinethe problem newline