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Convolutional Neural Networks Applied for Parkinson’s Disease Identification

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Machine Learning for Health Informatics

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9605))

Abstract

Parkinson’s Disease (PD) is a chronic and progressive illness that affects hundreds of thousands of people worldwide. Although it is quite easy to identify someone affected by PD when the illness shows itself (e.g. tremors, slowness of movement and freezing-of-gait), most works have focused on studying the working mechanism of the disease in its very early stages. In such cases, drugs can be administered in order to increase the quality of life of the patients. Since the beginning, it is well-known that PD patients feature the micrography, which is related to muscle rigidity and tremors. As such, most exams to detect Parkinson’s Disease make use of handwritten assessment tools, where the individual is asked to perform some predefined tasks, such as drawing spirals and meanders on a template paper. Later, an expert analyses the drawings in order to classify the progressive of the disease. In this work, we are interested into aiding physicians in such task by means of machine learning techniques, which can learn proper information from digitized versions of the exams, and them recommending a probability of a given individual being affected by PD depending on its handwritten skills. Particularly, we are interested in deep learning techniques (i.e. Convolutional Neural Networks) due to their ability into learning features without human interaction. Additionally, we propose to fine-tune hyper-arameters of such techniques by means of meta-heuristic-based techniques, such as Bat Algorithm, Firefly Algorithm and Particle Swarm Optimization.

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Notes

  1. 1.

    http://wwwp.fc.unesp.br/~papa/pub/datasets/Handpd/.

  2. 2.

    The CNN hyper-parameters in this case are the default values given by Caffe [39].

  3. 3.

    A random search means an aleatory initialization of hyper-parameters between the range bounds.

  4. 4.

    Notice these values have been empirically setup.

  5. 5.

    http://caffe.berkeleyvision.org.

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Acknowledgments

The authors are grateful to FAPESP grants #2014/16250-9 and #2015/25739-4, as well as CNPq grant #306166/2014-3.

Author information

Authors and Affiliations

  1. Department of Computing, Federal University of São Carlos, São Carlos, Brazil

    Clayton R. Pereira

  2. Department of Computing, São Paulo State University, Bauru, Brazil

    Danillo R. Pereira, Joao P. Papa & Gustavo H. Rosa

  3. School of Science and Technology, Middlesex University, London, UK

    Xin-She Yang

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  1. Clayton R. Pereira
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  2. Danillo R. Pereira
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  3. Joao P. Papa
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  4. Gustavo H. Rosa
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Correspondence to Joao P. Papa .

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Editors and Affiliations

  1. Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz, Austria

    Andreas Holzinger

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Pereira, C.R., Pereira, D.R., Papa, J.P., Rosa, G.H., Yang, XS. (2016). Convolutional Neural Networks Applied for Parkinson’s Disease Identification. In: Holzinger, A. (eds) Machine Learning for Health Informatics. Lecture Notes in Computer Science(), vol 9605. Springer, Cham. https://doi.org/10.1007/978-3-319-50478-0_19

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  • DOI: https://doi.org/10.1007/978-3-319-50478-0_19

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