Parkinson’s disease detection through hand drawings using Alexnet model
1
Department of Electronics and Telecommunications, University of Kasdi Merbah, Algeria
Submission date: 2024-03-06
Acceptance date: 2024-08-09
Online publication date: 2024-09-23
Publication date: 2024-09-23
Corresponding author
Abderrazak Benchabane
Department of Electronics and Telecommunications, University of Kasdi Merbah, Algeria
Department of Electronics and Telecommunications, University of Kasdi Merbah, Algeria
KEYWORDS
TOPICS
ABSTRACT
Innovative methods based on deep learning and different types of data are very useful in diagnosing Parkinson's disease. Through this research, we present a new method for detecting Parkinson's disease using deep convolutional neural networks based on the AlexNet architecture. The proposed approach focuses on using hand drawings of injured or suspected people and then integrating the features extracted from these drawings to classify subjects. To evaluate the proposed method, we used spiral and wave patterns available in the Kaggle's repository. This database is obtained from patients with Parkinson's disease and healthy individuals. By combining the features of the two different hand drawing styles, we were able to significantly improve detection accuracy. The related experimental results show the effectiveness of this approach, as it achieved an accuracy of 96.67% on the test set, outperforming current methods that use fusion in classifiers to make decisions, which achieved an accuracy of 93.33%. However, the challenge of achieving a higher accuracy highlights the complexity of diagnosing Parkinson's disease from manual drawings.
FUNDING
This research received no external funding.
REFERENCES (18)
1.
Mei J, Desrosiers C, Frasnelli J. Machine learning for the diagnosis of Parkinson’s disease: A Review of Literature. Frontiers in Aging Neuroscience 2021; 13: 633752. https://doi.org/10.3389/fnagi.....
2.
Kamran I, Naz S, Razzak I, Imran M. Handwriting dynamics assessment using deep neural network for early identification of Parkinson’s disease. Future Generation Computer Systems 2021; 117: 234–44. https://doi.org/10.1016/j.futu....
3.
Adeli E, Shi F, An L, Wee CY, Wu G, Wang T, et al. Joint feature-sample selection and robust diagnosis of Parkinson’s disease from MRI data. NeuroImage 2016; 141: 206–19. https://doi.org/10.1016/j.neur....
4.
Kotsavasiloglou C, Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M. Machine learning-based classification of simple drawing movements in Parkinson’s disease. Biomedical Signal Processing and Control 2017; 31: 174–80. https://doi.org/10.1016/j.bspc....
5.
Das HS, Das A, Neog A, Mallik S, Bora K, Zhao Z. Early detection of Parkinson’s disease using fusion of discrete wavelet transformation and histograms of oriented gradients. Mathematics 2022; 10(22): 4218. https://doi.org/10.3390/math10....
6.
Rana A, Dumka A, Singh R, Panda MK, Priyadarshi N, Twala B. Imperative role of machine learning algorithm for detection of Parkinson’s disease: review, challenges and recommendations. Diagnostics (Basel, Switzerland) 2022; 12(8): 2003. https://doi.org/10.3390/diagno....
7.
S. Kanakaprabha, P. Arulprakash, R. S. Parkinson disease detection using various machine learning algorithms. 2022 International Conference on Advanced Computing Technologies and Applications (ICACTA) 2022; 1–6. https://doi.org/10.1109/ICACTA....
8.
R ND, A V, S AR, E A, G S. Detecting Parkinson’s disease using machine learning. 2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) 2023; 1–6. https://doi.org/10.1109/ICECON....
9.
Shaikh M, Tilekar M, Pawar M, Suryawanshi M, Talhar M. Parkinson disease detection from spiral and wave drawings using machine learning algorithm. International Journal of Advanced Research in Science, Communication and Technology 2022: 252–8. https://doi.org/10.48175/IJARS....
10.
Islam MdA, Hasan Majumder MdZ, Hussein MdA, Hossain KM, Miah MdS. A review of machine learning and deep learning algorithms for Parkinson’s disease detection using handwriting and voice datasets. Heliyon 2024; 10(3): e25469. https://doi.org/10.1016/j.heli....
11.
Alniemi O, Mahmood H. Convolutional neural network for the detection of Parkinson disease based on hand-draw spiral images. Indonesian Journal of Electrical Engineering and Computer Science 2023; 30: 267. https://doi.org/10.11591/ijeec....
12.
Zham P, Arjunan S, Raghav S, Kumar D. Efficacy of Guided Spiral Drawing in the Classification of Parkinson’s Disease. IEEE Journal of Biomedical and Health Informatics 2017; 1–1. https://doi.org/10.1109/JBHI.2....
13.
Sivakumar M, Christinal H, Jebasingh S. Parkinson’s disease diagnosis using a combined deep learning approach. 2021; 81–4. https://doi.org/10.1109/ICSPC5....
14.
Ferdib-Al-Islam, Akter L. Early Identification of Parkinson’s disease from hand-drawn images using histogram of oriented gradients and machine learning techniques. 2020 Emerging Technology in Computing, Communication and Electronics (ETCCE) 2020; 1–6. https://doi.org/10.1109/ETCCE5....
15.
Das A, Das H, Choudhury A, Neog A, Mazumdar S. Detection of Parkinson’s disease from hand-drawn images using deep transfer learning. 2021; 67–84. https://doi.org/10.1007/978-98....
16.
Chakraborty S, Aich S, Jong-Seong-Sim, Han E, Park J, Kim HC. Parkinson’s disease detection from spiral and wave drawings using convolutional neural networks: a multistage classifier approach. 2020 22nd International Conference on Advanced Communication Technology (ICACT) 2020; 298–303. https://doi.org/10.23919/ICACT....
17.
Zham P, Kumar DK, Dabnichki P, Poosapadi Arjunan S, Raghav S. Distinguishing different stages of Parkinson’s disease using composite index of speed and pen-pressure of sketching a spiral. Frontiers in Neurology 2017; https://doi.org/10.3389/fneur.....
18.
Krizhevsky A, Sutskever I, Hinton G. ImageNet classification with deep convolutional neural networks. Neural Information Processing Systems 2012; 25. https://doi.org/10.1145/306538....
| eISSN: | 2449-5220 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
