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An Improved Pathological Brain Detection System Based on Two-Dimensional PCA and Evolutionary Extreme Learning Machine

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Pathological brain detection has made notable stride in the past years, as a consequence many pathological brain detection systems (PBDSs) have been proposed. But, the accuracy of these systems still needs significant improvement in order to meet the necessity of real world diagnostic situations. In this paper, an efficient PBDS based on MR images is proposed that markedly improves the recent results. The proposed system makes use of contrast limited adaptive histogram equalization (CLAHE) to enhance the quality of the input MR images. Thereafter, two-dimensional PCA (2DPCA) strategy is employed to extract the features and subsequently, a PCA+LDA approach is used to generate a compact and discriminative feature set. Finally, a new learning algorithm called MDE-ELM is suggested that combines modified differential evolution (MDE) and extreme learning machine (ELM) for segregation of MR images as pathological or healthy. The MDE is utilized to optimize the input weights and hidden biases of single-hidden-layer feed-forward neural networks (SLFN), whereas an analytical method is used for determining the output weights. The proposed algorithm performs optimization based on both the root mean squared error (RMSE) and norm of the output weights of SLFNs. The suggested scheme is benchmarked on three standard datasets and the results are compared against other competent schemes. The experimental outcomes show that the proposed scheme offers superior results compared to its counterparts. Further, it has been noticed that the proposed MDE-ELM classifier obtains better accuracy with compact network architecture than conventional algorithms.

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Notes

  1. Note: Condition number is shown to be an effective qualitative measure to find the conditioning of a matrix [44]. It may be noted that an ill-conditioned system has large condition number, while a well-conditioned system has small condition number. The 2-norm condition number of the matrix H can be calculated as,

    $$ \mathcal{K}_{2}(\mathbf{H})=\sqrt{\frac{\lambda_{max}(\mathbf{H}^{T} \mathbf{H})}{\lambda_{min}(\mathbf{H}^{T}\mathbf{H})}} $$
    (8)

    where, λ m a x (H T H) and λ m i n (H T H) denotes the largest and smallest eigenvalues of matrix H T H.

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Authors and Affiliations

  1. Pattern Recognition Lab, Department of Computer Science and Engineering, National Institute of Technology, Rourkela, 769 008, India

    Deepak Ranjan Nayak, Ratnakar Dash & Banshidhar Majhi

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  1. Deepak Ranjan Nayak
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  2. Ratnakar Dash
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Correspondence to Deepak Ranjan Nayak.

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This article is part of the Topical Collection on Advanced Computational Intelligence and Soft Computing in Medical Imaging

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Nayak, D.R., Dash, R. & Majhi, B. An Improved Pathological Brain Detection System Based on Two-Dimensional PCA and Evolutionary Extreme Learning Machine. J Med Syst 42, 19 (2018). https://doi.org/10.1007/s10916-017-0867-4

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