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Soft Computing and Signal Processing pp 241–252Cite as

Detection of Parkinson’s Disease from Hand-Drawn Images Using Machine Learning Algorithms

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1325)

Abstract

The diagnosis of Parkinson’s disease is very costly. With early detection of this disease, with proper medication, a patient can lead a better life. In this paper, the aim is to simplify the process for detection of Parkinson’s disease by relying only on hand-drawn figures taken from the disease-affected patients. Two different strategies have been employed to verify the efficiency of the proposed approaches. Histogram of oriented gradients features as well as deep features has also been extracted for different types of hand-drawn images, which act as an input to various machine learning classifiers such as k-nearest neighbor, random forest, support vector machine, Naïve Bayes, and multi-layer perceptron, respectively. This paper includes the analysis of the performance of handcrafted feature against deep level features. Experimental results show that for dataset 1 and dataset 2 has achieved an accuracy of 93% and 98%, respectively.

Keywords

  • Parkinson’s disease
  • Histogram of oriented features
  • Deep features
  • Machine learning techniques

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  • DOI: 10.1007/978-981-33-6912-2_22
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Acknowledgements

This work is a part of the project sanctioned by Assam Science and Technology University (ASTU), Guwahati under the Collaborative Research scheme of TEQIP-III via grant no. ASTU/TEQIP-III/Collaborative Research/2019/2479.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Girijananda Chowdhury Institute of Management and Technology, Guwahati, 781017, Assam, India

    Akalpita Das

  2. Department of Computer Science and Information Technology, Cotton University, Guwahati, 781001, Assam, India

    Himanish Shekhar Das

  3. Department of Computer Science and Engineering, Jorhat Engineering College, Jorhat, 785007, Assam, India

    Anupal Neog, B. Bharat Reddy, Arijeet Choudhury & Mrinoy Swargiary

Authors
  1. Akalpita Das
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  2. Himanish Shekhar Das
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  3. Anupal Neog
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  4. B. Bharat Reddy
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  5. Arijeet Choudhury
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  6. Mrinoy Swargiary
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Corresponding author

Correspondence to Himanish Shekhar Das .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Malla Reddy College of Engineering and Technology, Secunderabad, India

    Dr. V. Sivakumar Reddy

  2. Department of Computer Science and Engineering, Jawaharlal Nehru Technological University Hyderabad (JNTUH), Hyderabad, India

    Prof. V. Kamakshi Prasad

  3. Department Computer Science and Software Engineering, Monmouth University, West Long Branch, NJ, USA

    Jiacun Wang

  4. Department of Electronics and Communication Engineering, Sir Visvesvaraya Institute of Technology, Nashik, India

    Dr. K. T. V. Reddy

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Das, A., Das, H.S., Neog, A., Bharat Reddy, B., Choudhury, A., Swargiary, M. (2021). Detection of Parkinson’s Disease from Hand-Drawn Images Using Machine Learning Algorithms. In: Reddy, V.S., Prasad, V.K., Wang, J., Reddy, K.T.V. (eds) Soft Computing and Signal Processing. Advances in Intelligent Systems and Computing, vol 1325. Springer, Singapore. https://doi.org/10.1007/978-981-33-6912-2_22

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