Convolutional Neural Networks Applied for Parkinson’s Disease Identification

  • Clayton R. Pereira
  • Danillo R. Pereira
  • Joao P. Papa
  • Gustavo H. Rosa
  • Xin-She Yang
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Convolutional Neural Networks Parkinson’s Disease Machine learning Meta-heuristics 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Clayton R. Pereira
    • 1
  • Danillo R. Pereira
    • 2
  • Joao P. Papa
    • 2
  • Gustavo H. Rosa
    • 2
  • Xin-She Yang
    • 3
  1. 1.Department of ComputingFederal University of São CarlosSão CarlosBrazil
  2. 2.Department of ComputingSão Paulo State UniversityBauruBrazil
  3. 3.School of Science and TechnologyMiddlesex UniversityLondonUK

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