A novel data mining approach for detection of brain disorder diseases using an integrated wavelet transform technique

Abstract

An integrated form of wavelet transform technique (IDWT) has been explored and established as a feature extraction strategy to detect the brain disorder from brain MRI images is this study. There are different orientations of human brain images such as; sagittal, coronal and transaxial plane. Here, transaxial plane human brain images are collected and the advantages of Haar and Bi-orthogonal 1.3 wavelet functions are used to extract the relevant features in two-dimensional space. After features are extracted from two different integrated wavelet functions, then the entire combined feature matrix has been provided as an input to Support Vector Machine (SVM) with linear and polynomial kernel to classify different brain disorder diseases. This proposed integrated feature extraction strategy has been compared with the multi-layer probabilistic neural network (MLPNN), Naive Bayesian network and Logistic Regression as well as existing techniques studied during the literature survey and it has been observed that proposed DWT with SVM polynomial kernel is giving 100% result, additionally, the proposed model has been validated using accuracy, precision, recall and F1-score.