A Multilayer Perceptron Based Smart Pathological Brain Detection System by Fractional Fourier Entropy.
This work aims at developing a novel pathological brain detection system (PBDS) to assist neuroradiologists to interpret magnetic resonance (MR) brain images. We simplify this problem as recognizing pathological brains from healthy brains. First, 12 fractional Fourier entropy (FRFE) features were ex...
| Publicado en: | Journal of Medical Systems Vol. 40; no. 7; pp. 1 - 12 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2016
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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=115925366&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115925366 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jul2016 vid: 40 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115925366 115925366 115925366 10.1007/s10916-016-0525-2 115925366 ppf: 1 ppct: 11 formats: fmt: @attributes: type: P tig: atl: A Multilayer Perceptron Based Smart Pathological Brain Detection System by Fractional Fourier Entropy. aug: au: Zhang, Yudong Sun, Yi Phillips, Preetha Liu, Ge Zhou, Xingxing Wang, Shuihua affil: School of Natural Sciences and Mathematics, Shepherd University, Shepherdstown 25443 USA sug: subj: Brain Diseases Diagnosis Magnetic Resonance Imaging Methods Brain Pathology Multilayer Perceptrons Human Neurons Image Processing, Computer Assisted kappa Statistic Descriptive Statistics Brain Neoplasms Diagnosis Neurodegenerative Diseases Diagnosis Hematoma, Subdural Diagnosis Multiple Sclerosis Diagnosis Pick Disease of the Brain Diagnosis Agnosia Diagnosis Funding Source ab: This work aims at developing a novel pathological brain detection system (PBDS) to assist neuroradiologists to interpret magnetic resonance (MR) brain images. We simplify this problem as recognizing pathological brains from healthy brains. First, 12 fractional Fourier entropy (FRFE) features were extracted from each brain image. Next, we submit those features to a multi-layer perceptron (MLP) classifier. Two improvements were proposed for MLP. One improvement is the pruning technique that determines the optimal hidden neuron number. We compared three pruning techniques: dynamic pruning (DP), Bayesian detection boundaries (BDB), and Kappa coefficient (KC). The other improvement is to use the adaptive real-coded biogeography-based optimization (ARCBBO) to train the biases and weights of MLP. The experiments showed that the proposed FRFE + KC-MLP + ARCBBO achieved an average accuracy of 99.53 % based on 10 repetitions of K-fold cross validation, which was better than 11 recent PBDS methods. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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