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Journal of Medical Systems
Published in: Journal of Medical Systems 7/2016

01-07-2016 | Systems-Level Quality Improvement

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

Authors: Yudong Zhang, Yi Sun, Preetha Phillips, Ge Liu, Xingxing Zhou, Shuihua Wang

Published in: Journal of Medical Systems | Issue 7/2016

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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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Metadata
Title
A Multilayer Perceptron Based Smart Pathological Brain Detection System by Fractional Fourier Entropy
Authors
Yudong Zhang
Yi Sun
Preetha Phillips
Ge Liu
Xingxing Zhou
Shuihua Wang
Publication date
01-07-2016
Publisher
Springer US
Published in
Journal of Medical Systems / Issue 7/2016
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-016-0525-2

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