Classification and segmentation of brain tumor using Adaboost classifier

Abnormal or uncontrollable growth of a tissue within the brain is known as a brain tumor. The tumor detection, classification and investigate by experts is a usual technique for brain tumor classification from MRI. Explicit classification of brain MRI and interpretation of brain images organized and various techniques has been proposed. The proposed method for classification of Brain Tumor using Classifier and Neural Network uses segmentation algorithms which include Skull Striping along with morphological operation aiming that preprocessing, Anisotropic Diffusion Filtering uses for noise removal and smoothing with edge detection locating the boundaries anatomically of the image. Secondly, wavelet transforms used in the decomposition and the feature extraction of MRI and combining features of MRI images. Finally, Adaboost uses classification techniques, learn by selecting discriminative features proposed to classify MRI images into tumorous (malignant) and non-tumorous (benign). Experimental results show 100% accuracy for 155 MRI images.