IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v114y2018icp164-174.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077918304909
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2018.06.034?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Scabini, Leonardo F.S. & Bruno, Odemir M., 2023. "Structure and performance of fully connected neural networks: Emerging complex network properties," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    2. Soriano-Sánchez, A.G. & Posadas-Castillo, C., 2018. "Smart pattern to generate small–world networks," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 415-422.
    3. Shabestari, Payam Sadeghi & Panahi, Shirin & Hatef, Boshra & Jafari, Sajad & Sprott, Julien C., 2018. "A new chaotic model for glucose-insulin regulatory system," Chaos, Solitons & Fractals, Elsevier, vol. 112(C), pages 44-51.
    4. Panahi, Shirin & Aram, Zainab & Jafari, Sajad & Ma, Jun & Sprott, J.C., 2017. "Modeling of epilepsy based on chaotic artificial neural network," Chaos, Solitons & Fractals, Elsevier, vol. 105(C), pages 150-156.
    5. Mostaghimi, Soudeh & Nazarimehr, Fahimeh & Jafari, Sajad & Ma, Jun, 2019. "Chemical and electrical synapse-modulated dynamical properties of coupled neurons under magnetic flow," Applied Mathematics and Computation, Elsevier, vol. 348(C), pages 42-56.
    6. He, Haoming & Xiao, Min & Lu, Yunxiang & Wang, Zhen & Tao, Binbin, 2023. "Control of tipping in a small-world network model via a novel dynamic delayed feedback scheme," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    7. Latifoğlu, Fatma & İleri, Ramis & Demirci, Esra, 2021. "Assessment of dyslexic children with EOG signals: Determining retrieving words/re-reading and skipping lines using convolutional neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    8. Sabouri, Amir & Ghasemi, Mahdieh & Mehrabbeik, Mahtab, 2023. "The dynamical analysis of non-uniform neocortical network model in up-down state oscillations," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    9. Matjaž Perc & Mahmut Ozer & Janja Hojnik, 2019. "Social and juristic challenges of artificial intelligence," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-7, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:114:y:2018:i:c:p:164-174. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.