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Impact of hybrid neural network on the early diagnosis of diabetic retinopathy disease from video-oculography signals

Author

Listed:
  • Kaya, Ceren
  • Erkaymaz, Okan
  • Ayar, Orhan
  • Özer, Mahmut

Abstract

In this study, we introduce two hybrid artificial neural network models with particle swarm optimization algorithm to diagnose diabetic retinopathy based on the Video-Oculography signals. The hybrid models use Discrete Wavelet Transform and Hilbert-Huang Transform separately to extract features from the signals. The classification performance of both models is analyzed comparatively. We show that the model based on Hilbert–Huang Transform exhibits better classification performance than the model based on the Discrete Wavelet Transform.

Suggested Citation

  • Kaya, Ceren & Erkaymaz, Okan & Ayar, Orhan & Özer, Mahmut, 2018. "Impact of hybrid neural network on the early diagnosis of diabetic retinopathy disease from video-oculography signals," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 164-174.
    • Handle: RePEc:eee:chsofr:v:114:y:2018:i:c:p:164-174
    DOI: 10.1016/j.chaos.2018.06.034
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    References listed on IDEAS

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    1. Erkaymaz, Okan & Ozer, Mahmut & Perc, Matjaž, 2017. "Performance of small-world feedforward neural networks for the diagnosis of diabetes," Applied Mathematics and Computation, Elsevier, vol. 311(C), pages 22-28.
    2. Erkaymaz, Okan & Ozer, Mahmut, 2016. "Impact of small-world network topology on the conventional artificial neural network for the diagnosis of diabetes," Chaos, Solitons & Fractals, Elsevier, vol. 83(C), pages 178-185.
    3. Erkaymaz, Hande & Ozer, Mahmut & Orak, İlhami Muharrem, 2015. "Detection of directional eye movements based on the electrooculogram signals through an artificial neural network," Chaos, Solitons & Fractals, Elsevier, vol. 77(C), pages 225-229.
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