Early diagnosis of primary tumor in brain MRI images using wavelet as the input of Ada-Boost classifier

In this paper we have developed a new approach for automatic classification of brain tumor in enhanced MRI images. The proposed method consists of four stages namely Preprocessing, feature extraction, feature reduction and classification. In the first stage wiener filter is applied for noise reduction and to make the image suitable for extracting the features. In the second stage, the seeded region growing segmentation is used for partitioning the image into meaningful regions. In the third stage, Discrete wavelet transformation is used to extract the wavelet coefficients from the segmented image. In the next stage PCA is used to reduce the dimensionality of the wavelet coefficients which results in a more efficient and accurate classification. Finally, In the classification stage, Ada-Boost classifier is used to classify the experimental images into normal and abnormal cases. Our proposed method is evaluated using the metrics sensitivity, specificity and accuracy. It produces better results compared to Linear and non-linear SVM.