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A Multilayer Perceptron Based Smart Pathological Brain Detection System by Fractional Fourier Entropy

  • Systems-Level Quality Improvement
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Abstract

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.

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Acknowledgments

This paper was supported by NSFC (51407095, 61503188), Natural Science Foundation of Jiangsu Province (BK20150983), Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing (BM2013006), Program of Natural Science Research of Jiangsu Higher Education Institutions (15KJB470010), Nanjing Normal University Research Foundation for Talented Scholars (2013119XGQ0061, 2014119XGQ0080), Open Fund of Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology (15-140-30-008 K), Open Project Program of the State Key Lab of CAD&CG, Zhejiang University (A1616), Open Fund of Key laboratory of symbolic computation and knowledge engineering of ministry of education, Jilin University (93K172016K17), Open Fund of Key Laboratory of Statistical information technology and data mining, State Statistics Bureau, (SDL201608), Fundamental Research Funds for the Central Universities (LGYB201604).

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Correspondence to Shuihua Wang.

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This article is part of the Topical Collection on Systems-Level Quality Improvement

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Zhang, Y., Sun, Y., Phillips, P. et al. A Multilayer Perceptron Based Smart Pathological Brain Detection System by Fractional Fourier Entropy. J Med Syst 40, 173 (2016). https://doi.org/10.1007/s10916-016-0525-2

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