A Light-weight CNN Model for Efficient Parkinson's Disease Diagnostics
This work addresses efficient diagnostics for Parkinson's disease patients, but it appears incremental as it combines existing CNN and LSTM techniques for a specific medical application.
The paper tackles Parkinson's disease diagnostics by proposing a light-weight CNN-LSTM model for classifying hand-drawn time-series data, achieving high-quality results with fewer parameters and outperforming conventional methods like SVM, RF, lightgbm, and CNN-based approaches.
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.