Single slice based detection for alzheimer’s disease via wavelet entropy and multilayer perceptron trained by biogeography-based optimization

Shui Hua Wang, Yin Zhang, Yu Jie Li, Wen Juan Jia, Fang Yuan Liu, Meng Meng Yang, Yu Dong Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

107 Citations (Scopus)

Abstract

Detection of Alzheimer’s disease (AD) from magnetic resonance images can help neuroradiologists to make decision rapidly and avoid missing slight lesions in the brain. Currently, scholars have proposed several approaches to automatically detect AD. In this study, we aimed to develop a novel AD detection system with better performance than existing systems. 28 ADs and 98 HCs were selected from OASIS dataset. We used inter-class variance criterion to select single slice from the 3D volumetric data. Our classification system is based on three successful components: wavelet entropy, multilayer perceptron, and biogeography-base optimization. The statistical results of our method obtained an accuracy of 92.40 ± 0.83%, a sensitivity of 92.14 ± 4.39%, a specificity of 92.47 ± 1.23%. After comparison, we observed that our pathological brain detection system is superior to latest 6 other approaches.

Original languageEnglish
Pages (from-to)10393-10417
Number of pages25
JournalMultimedia Tools and Applications
Volume77
Issue number9
DOIs
Publication statusPublished - May 2018
Externally publishedYes

Keywords

  • Alzheimer’s disease
  • Biogeography-based optimization
  • Inter-class variance
  • Multilayer perceptron
  • Pathological brain detection
  • Wavelet entropy

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