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.
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Al Shareef, E., Ozsahin, D.U. (2019). Deep Parkinson Disease Diagnosis: Stacked Auto-encoder. In: Aliev, R., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Sadikoglu, F. (eds) 13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing — ICAFS-2018. ICAFS 2018. Advances in Intelligent Systems and Computing, vol 896. Springer, Cham. https://doi.org/10.1007/978-3-030-04164-9_76
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DOI: https://doi.org/10.1007/978-3-030-04164-9_76
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