Abstract
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.
C.R. Pereira and L.A. Passos—Both authors contributed equally.
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Notes
- 1.
Available in http://wwwp.fc.unesp.br/~papa/pub/datasets/Handpd/.
- 2.
Notice the range values were empirically chosen.
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Acknowledgments
The authors would like to thank FAPESP grants #2014/16250-9, #2014/12236-1, #2015/25739-4 and #2016/21243-7, as well as Capes, and CNPq grant #306166/2014-3.
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Pereira, C.R., Passos, L.A., Lopes, R.R., Weber, S.A.T., Hook, C., Papa, J.P. (2017). Parkinson’s Disease Identification Using Restricted Boltzmann Machines. In: Felsberg, M., Heyden, A., Krüger, N. (eds) Computer Analysis of Images and Patterns. CAIP 2017. Lecture Notes in Computer Science(), vol 10425. Springer, Cham. https://doi.org/10.1007/978-3-319-64698-5_7
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