Development of pathological brain detection system using Jaya optimized improved extreme learning machine and orthogonal ripplet-II transform

Pathological brain detection systems (PBDSs) have drawn much attention from researchers over the past two decades because of their significance in taking correct clinical decisions. In this paper, an efficient PBDS based on MR images is introduced that markedly improves the recent results. The proposed system makes use of contrast limited adaptive histogram equalization (CLAHE) and orthogonal discrete ripplet-II transform (O-DR2T) with degree 2 to enhance the quality of the input MR images and extract the features respectively. Subsequently, relevant features are obtained using PCA+LDA approach. Finally, a novel learning algorithm called IJaya-ELM is proposed that combines improved Jaya algorithm (IJaya) and extreme learning machine (ELM) for segregation of MR images as pathological or healthy. The improved Jaya algorithm is utilized to optimize the input weights and hidden biases of single-hidden-layer feedforward neural networks (SLFN), whereas one analytical method is used for determining the output weights. The proposed algorithm performs optimization according to both the root mean squared error (RMSE) and the norm of the output weights of SLFNs. Extensive experiments are carried out using three benchmark datasets and the results are compared against other competent schemes. The experimental results demonstrate that the proposed scheme brings potential improvements in terms of classification accuracy and number of features. Moreover, the proposed IJaya-ELM classifier achieves higher accuracy and obtains compact network architecture compared to conventional ELM and BPNN classifier.