This paper presents an automatic classification system for segregating pathological brain
from normal brains in magnetic resonance imaging scanning. The proposed system employs contrast
limited adaptive histogram equalization scheme to enhance the diseased region in brain MR images.
Two-dimensional stationary wavelet transform is harnessed to extract features from the preprocessed
images. The feature vector is constructed using the energy and entropy values, computed from the level-
2 SWT coefficients. Then, the relevant and uncorrelated features are selected using symmetric uncertainty
ranking filter. Subsequently, the selected features are given input to the proposed AdaBoost with
support vector machine classifier, where SVM is used as the base classifier of AdaBoost algorithm. To
validate the proposed system, three standard MR image datasets, Dataset-66, Dataset-160, and Dataset-
255 have been utilized. The 5 runs of k-fold stratified cross validation results indicate the suggested
scheme offers better performance than other existing schemes in terms of accuracy and number of features.
The proposed system earns ideal classification over Dataset-66 and Dataset-160; whereas, for Dataset-
255, an accuracy of 99.45% is achieved.
Keywords: AdaBoost with SVM, computer-aided diagnosis, contrast limited adaptive histogram equalization, magnetic resonance
imaging, stationary wavelet transform.
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