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)


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.


Deep learning Stacked auto-encoder Parkinson’s disease 


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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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