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Parkinson’s Disease Identification Using Restricted Boltzmann Machines

  • Currently, Parkinson’s Disease (PD) has no cure or accurate diagnosis, reaching approximately 60, 000 new cases yearly and worldwide, being more often in the elderly population. Its main symptoms can not be easily uncorrelated with other illness, being way more difficult to be identified at the early stages. As such, computer-aided tools have been recently used to assist in this task, but the challenge in the automatic identification of Parkinson’s Disease still persists. In order to cope with this problem, we propose to employ Restricted Boltzmann Machines (RBMs) to learn features in an unsupervised fashion by analyzing images from handwriting exams, which aim at assessing the writing skills of potential individuals. These are one of the main symptoms of PD-prone people, since such kind of ability ends up being severely affected. We show that RBMs can learn proper features that help supervised classifiers in the task of automatic identification of PD patients, as well as one can obtain a more compact representation of the exam for the sake of storage and computational load purposes.

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Metadaten
Author:Clayton R. PereiraORCiD, Leandro A. Passos, Ricardo R. Lopes, Silke A. T. WeberORCiD, Christian Hook, Joao Paulo PapaORCiD
DOI:https://doi.org/10.1007/978-3-319-64698-5_7
ISBN:978-3-319-64697-8
Parent Title (English):Computer Analysis of Images and Patterns, 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part II
Publisher:Springer
Document Type:Part of a Book
Language:English
Year of first Publication:2017
Release Date:2022/07/11
Tag:Bevölkerung; Handschrift; maschinelles Lernen
Volume:10425
First Page:70
Last Page:80
Andere Schriftenreihe:Lecture Notes in Computer Science ; 10425
Institutes:Fakultät Informatik und Mathematik
research focus:Digitalisierung
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG