An Improved Pathological Brain Detection System Based on Two-Dimensional PCA and Evolutionary Extreme Learning Machine.
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...
| Publicado en: | Journal of Medical Systems Vol. 42; no. 1; pp. 1 - 16 |
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| Autores principales: | , , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jan2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=127145169&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 127145169 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jan2018 vid: 42 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 127145169 127145169 127145169 10.1007/s10916-017-0867-4 127145169 ppf: 1 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An Improved Pathological Brain Detection System Based on Two-Dimensional PCA and Evolutionary Extreme Learning Machine. aug: au: Nayak, Deepak Ranjan Dash, Ratnakar Majhi, Banshidhar affil: Pattern Recognition Lab, Department of Computer Science and Engineering, National Institute of Technology, 769 008, Rourkela, India sug: subj: Factor Analysis Brain Physiopathology Algorithms Image Processing, Computer Assisted Extreme Learning Machines Human Experimental Studies Computer Simulation Machine Learning ab: 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. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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