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

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Soft Computing and Signal Processing


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.

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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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Correspondence to Himanish Shekhar Das .

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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.

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