Fractional wavelet transform based diagnostic system for brain tumor detection in MR imaging

Abstract

AbstractThe brain tumor detection is a highly complicated but significant task. The early detection of a brain tumor can increase the survival rate of an individual by providing proper treatment. This work proposes a computer‐aided diagnostic method for brain tumor detection using fractional wavelet transform (FrDWT) with different values of alpha (α) ranging from (0.1‐1), histogram‐based various local feature descriptors, feature selectors, and two classification methods, that is, support vector machine (SVM) as well as artificial neural network (ANN). The brain MR images dataset is taken from BraTS 2015, e‐health laboratory, and Harvard Medical School. The FrDWT and the local feature descriptors used are combined to extract features. Some of the features are selected using Eigenvector centrality and Laplacian Score techniques. The selected features are trained and classified by the two classifiers, SVM and ANN. It is a type of binary classification, so the labels provided to the classifier are named “normal” and “abnormal.” The performance is estimated using parameters like accuracy, sensitivity, precision, specificity, and F1 score. The results of FrDWT are compared with conventional discrete wavelet transform (DWT), and it is observed that FrDWT outperforms DWT at alpha (α) values lower than 0.5.