Skip to main content

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

  • 440 Accesses

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

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-981-33-6912-2_22
  • Chapter length: 12 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   219.00
Price excludes VAT (USA)
  • ISBN: 978-981-33-6912-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   279.99
Price excludes VAT (USA)
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. Mohamed GS (2016) Parkinson’s disease diagnosis: detecting the effect of attributes selection and discretization of Parkinson’s disease dataset on the performance of classifier algorithms. Open Access Lib J 3(11):1–11

    Google Scholar 

  2. Hariharan M, Polat K, Sindhu R (2014) A new hybrid intelligent system for accurate detection of Parkinson’s disease. Comput Methods Prog Biomed 113(3):904–913

    CrossRef  Google Scholar 

  3. Aich S, Younga K, Hui KL, Al-Absi AA, Sain M (2018) A nonlinear decision tree based classification approach to predict the Parkinson’s disease using different feature sets of voice data. In: 20th international conference on advanced communication technology (ICACT). IEEE, pp 638–642

    Google Scholar 

  4. Peker M, Sen B, Delen D (2015) Computer-aided diagnosis of Parkinson’s disease using complex-valued neural networks and mRMR feature selection algorithm. J Healthcare Eng 6(3):281–302

    CrossRef  Google Scholar 

  5. Andrade AO, Pereira AA, Soares MF, de Almeida GLC, Paixão APS, Fenelon SB, Dionisio VC (2013) Human tremor: origins, detection and quantification. In: Andrade AO (ed) Practical applications in biomedical engineering. InTech, Croatia

    CrossRef  Google Scholar 

  6. Drotár P, Mekyska J, Rektorová I, Masarová L, Smékal Z, Faundez-Zanuy M (2016) Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson’s disease. Artif Intell Med 67:39–46

    CrossRef  Google Scholar 

  7. Loconsole C, Trotta GF, Brunetti A, Trotta J, Schiavone A, Tatò SI, Losavio G, Bevilacqua V (2017) Computer vision and EMG-based handwriting analysis for classification in Parkinson’s disease. In: International conference on intelligent computing. Springer, pp 493–503

    Google Scholar 

  8. Pereira CR, Weber SA, Hook C, Rosa GH, Papa JP (2017) Deep learning-aided Parkinson’s disease diagnosis from handwritten dynamics. In: 29th SIBGRAPI conference on graphics, patterns and images (SIBGRAPI). IEEE, pp 340–346

    Google Scholar 

  9. Folador JP, Rosebrock A, Pereira AA, Vieira MF, de Oliveira Andrade A (2019) Classification of handwritten drawings of people with Parkinson’s disease by using histograms of oriented gradients and the random forest classifier. In: Latin American conference on biomedical engineering. Springer, Cham, pp 334–343

    Google Scholar 

  10. https://www.kaggle.com/kmader/parkinsons-drawings

  11. Zham P, Kumar DK, Dabnichki P, Poosapadi Arjunan S, Raghav S (2017) Distinguishing different stages of Parkinson’s disease using composite index of speed and pen-pressure of sketching a spiral. Frontiers Neurol 8:435

    CrossRef  Google Scholar 

  12. Bernardo LS, Quezada A, Munoz R, Maia FM, Pereira CR, Wu W, de Albuquerque VHC (2019) Handwritten pattern recognition for early Parkinson’s disease diagnosis. Pattern Recogn Lett 125:78–84

    CrossRef  Google Scholar 

  13. Athitsos V, Sclaroff S (2005) Boosting nearest neighbor classifiers for multiclass recognition. In: IEEE computer society conference on computer vision and pattern recognition (CVPR’05)—workshops. IEEE, pp 45–45

    Google Scholar 

  14. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    CrossRef  Google Scholar 

  15. Bustomi MA, Faricha A, Ramdhan A, Faridawati (2018) Integrated image processing analysis and naive Bayes classifier method for lungs X-ray image classification. ARPN J Eng Appl Sci 13(2):718–724

    Google Scholar 

  16. Kanafiah SNAM, Ali H, Firdaus AA, Azalan MZ, Jusman Y, Khairi AA, Ahmad MR, Sara T, Amran T, Mansor I, Shukor SAA (2019) Metal shape classification of buried object using multilayer perceptron neural network in GPR data. IOP Conf Ser Mater Sci Eng 705(1):012028. IOP Publishing

    Google Scholar 

  17. Bhattacharjee K, Pant M, Zhang YD, Satapathy SC (2020) Multiple instance learning with genetic pooling for medical data analysis. Pattern Recogn Lett 133:247–255

    CrossRef  Google Scholar 

  18. Praneel AV, Rao TS, Murty MR (2020) A survey on accelerating the classifier training using various boosting schemes within cascades of boosted ensembles. In: Reddy A, Marla D, Simic M, Favorskaya M, Satapathy S (eds) Intelligent manufacturing and energy sustainability, vol 169. Smart innovation, systems and technologies. Springer, Singapore, pp 809–825

    CrossRef  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Himanish Shekhar Das .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation