Title: Detection of Parkinson's disease using CNN

Authors: M. Kamesh; C. Augustine; D. Sarathy; S. Leopauline; Sheshang D. Degadwala

Addresses: Department of Electronics and Communication Engineering, GRT Institute of Engineering and Technology, Tiruttani, Tamil Nadu, India ' Department of Electronics and Communication Engineering, GRT Institute of Engineering and Technology, Tiruttani, Tamil Nadu, India ' Department of Electronics and Communication Engineering, GRT Institute of Engineering and Technology, Tiruttani, Tamil Nadu, India ' Department of Electronics and Communication Engineering, Veltech Multitech Dr. Rangarajan and Dr. Sakunthala Engineering College, Avadi, Chennai, India ' Department of Computer Engineering, Sigma Institute of Engineering, Vadodara, Gujarat, India

Abstract: Parkinson's disease can be diagnosed using computer-assisted diagnosis systems based on brain imaging, with the ultimate goal of finding patterns that characterise the disease. In this case, convolutional neural networks (CNNs) have proven to be extremely beneficial. Neurological disease, Parkinson's disease (PD), is characterised by a decrease in the brain's dopamine-producing neurons. Patients with Parkinson's disease have difficulty producing speech due to a lack of coordination in the muscles that control breathing, phonation, articulation, and prosody, among other things. Speech analysis can be used by clinicians to objectively assess the severity of Parkinson's disease in a non-invasive manner. In the LSTM layer, the output is then analysed for important temporal feature relationships. Existing state-of-the-art CNN models are compared to the proposed DenseNet-LSTM model. Training accuracy is 93.75%, testing accuracy is 90%, and validation accuracy is 93.87%, according to the suggested model.

Keywords: Parkinson's disease; Parkinson's speech; empirical mode decomposition; convolutional neural networks; CNNs; Parkinson's disease; machine learning; meta-heuristics.

DOI: 10.1504/IJMEI.2024.139889

International Journal of Medical Engineering and Informatics, 2024 Vol.16 No.4, pp.384 - 393

Received: 02 Jan 2022
Accepted: 03 Apr 2022
Published online: 09 Jul 2024 *

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