A Light-weight CNN Model for Efficient Parkinson's Disease Diagnostics

• URL: http://arxiv.org/abs/2302.00973v1
• Date: Thu, 2 Feb 2023 09:49:07 GMT
• Title: A Light-weight CNN Model for Efficient Parkinson's Disease Diagnostics
• Authors: Xuechao Wang, Junqing Huang, Marianna Chatzakou, Kadri Medijainen, Pille Taba, Aaro Toomela, Sven Nomm and Michael Ruzhansky
• Abstract summary: The proposed model consists of a convolution neural network (CNN) to short-term memory (LSTM) to adapt the characteristics of collected time-series signals. Experimental results show that the proposed model achieves a high-quality diagnostic result over multiple evaluation metrics with much fewer parameters and operations.
• Score: 1.382077805849933
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, deep learning methods have achieved great success in various fields due to their strong performance in practical applications. In this paper, we present a light-weight neural network for Parkinson's disease diagnostics, in which a series of hand-drawn data are collected to distinguish Parkinson's disease patients from healthy control subjects. The proposed model consists of a convolution neural network (CNN) cascading to long-short-term memory (LSTM) to adapt the characteristics of collected time-series signals. To make full use of their advantages, a multilayered LSTM model is firstly used to enrich features which are then concatenated with raw data and fed into a shallow one-dimensional (1D) CNN model for efficient classification. Experimental results show that the proposed model achieves a high-quality diagnostic result over multiple evaluation metrics with much fewer parameters and operations, outperforming conventional methods such as support vector machine (SVM), random forest (RF), lightgbm (LGB) and CNN-based methods.

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