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Deep Parkinson Disease Diagnosis: Stacked Auto-encoder

  • Esam Al Shareef
  • Dilber Uzun OzsahinEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 896)

Abstract

In this work, we demonstrate the feasibility of deep learning based stacked auto-encoder for the Parkinson’s Disease (PD) diagnosis. Features are extracted by the employed deep network from the input source. We transfer features learned from the SAE during pre-training to the fine-tuning phase in which each sample or patient’s condition is labeled, which grants the network the time to learn and distinguish the healthy from the PD patients. The employed model is fine-tuned and tested on a small dataset in order to explore their generalization capabilities when trained using few data. Experimentally, the stacked auto-encoder showed a high accuracy and features extraction capability in diagnosing the Parkinson diseased patients where it achieved an accuracy of 89.5% which is considered as a promising result.

Keywords

Deep learning Stacked auto-encoder Parkinson’s disease 

References

  1. 1.
    Hughes, A., Daniel, S., Kilford, L., Lees, A.: Accuracy of clinical diagnosis of idiopathic Parkinson’s disease: a clinico-pathological study of 100 cases. J. Neurol. Neurosurg. Psychiatry 55, 181–184 (1992)CrossRefGoogle Scholar
  2. 2.
    Elbaz, A., Bower, J., Maraganore, D., McDonnell, S., Peterson, B., Ahlskog, J., Schaid, D., Rocca, W.: Risk tables for parkinsonism and Parkinson’s disease. J. Clin. Epidemiol. 55, 25–31 (2002)CrossRefGoogle Scholar
  3. 3.
    Little, M., McSharry, P., Hunter, E., Spielman, J., Ramig, L.: Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease. IEEE Trans. Biomed. Eng. 56, 1015–1022 (2009)CrossRefGoogle Scholar
  4. 4.
    Tolosa, E., Wenning, G., Poewe, W.: The diagnosis of Parkinson’s disease. Lancet Neurol. 5, 75–86 (2006)CrossRefGoogle Scholar
  5. 5.
    Gil, D., Johnson, M.: Diagnosing Parkinson by using artificial neural networks and support vector machines. Global J. Comput. Sci. Technol. 9, 63–71 (2009)Google Scholar
  6. 6.
    Helwan, A., Uzun Ozsahin, D.: Sliding window based machine learning system for the left ventricle localization in MR cardiac images. Appl. Comput. Intell. Soft Comput. 2017, 1–9 (2017)CrossRefGoogle Scholar
  7. 7.
    Helwan, A., Uzun, D., Abiyev, R., Bush, J.: One-year survival prediction of myocardial infarction. Int. J. Adv. Comput. Sci. Appl. 8, 173–178 (2017)Google Scholar
  8. 8.
    Ozsahin, D., Isa, N., Uzun, B., Ozsahin, I.: Effective analysis of image reconstruction algorithms in nuclear medicine using fuzzy PROMETHEE. In: 2018 Advances in Science and Engineering Technology International Conferences (ASET) (2018)Google Scholar
  9. 9.
    Khemphila, A., Boonjing, V.: Parkinsons disease classification using neural network and feature selection. World Acad. Sci. Eng. Technol. Int. J. Math. Comput. Sci. 6, 377–380 (2012)Google Scholar
  10. 10.
    Baldi, P.: Autoencoders, unsupervised learning, and deep architectures. In: Proceedings of the 2011 International Conference on Unsupervised and Transfer Learning Workshop, UTLW 2011, vol. 27, pp. 37–50 (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringNear East UniversityNicosiaTurkey
  2. 2.Gordon Center for Medical Imaging, Radiology Massachusetts General Hospital and Harvard Medical SchoolBostonUSA

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