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, Volume 77, Issue 3, pp 3871–3885 | Cite as

Smart pathological brain detection system by predator-prey particle swarm optimization and single-hidden layer neural-network

Article

Abstract

In order to develop an artificial intelligence and computer-aided diagnosis system that assists neuroradiologists to interpret magnetic resonance (MR) images. This paper employed Hu moment invariant (HMI) as the brain image features, and we proposed a novel predator-prey particle swarm optimization (PP-PSO) algorithm used to train the weights of single-hidden layer neural-network (SLN). We used five-fold stratified cross validation (FFSCV) for statistical analysis. Our proposed HMI + SLN + PP-PSO method achieved a sensitivity of 96.00 ± 5.16%, a specificity of 98.57 ± 0.75%, and an accuracy of 98.25 ± 0.65% for the DA-160 dataset, and yields a sensitivity of 97.14 ± 2.33%, a specificity of 97.00 ± 0.34%, and an accuracy of 97.02 ± 0.33% for the DA-255 dataset. Our method performs better than six state-of-the-art approaches.

Keyword

Predator-prey particle swarm optimization Computer-aided diagnosis Magnetic resonance imaging Single-hidden layer neural-network 

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of Computer Science and Technology & School of Education ScienceNanjing Normal UniversityNanjingChina
  2. 2.Department of NeurologyFirst Affiliated Hospital of Nanjing Medical UniversityNanjingChina
  3. 3.School of Information EngineeringYangzhou UniversityYangzhouChina
  4. 4.School of Information and Safety EngineeringZhongnan University of Economics and LawWuhanChina

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