online read us now
Paper details
Number 4 - December 2021
Volume 31 - 2021
A hybrid two-stage SqueezeNet and support vector machine system for Parkinson’s disease detection based on handwritten spiral patterns
Lucas Salvador Bernardo, Robertas Damaševičius, Victor Hugo C. de Albuquerque, Rytis Maskeliūnas
Abstract
Parkinson’s disease (PD) is the second most common neurological disorder in the world. Nowadays, it is estimated that
it affects from 2% to 3% of the global population over 65 years old. In clinical environments, a spiral drawing task is
performed to help to obtain the disease’s diagnosis. The spiral trajectory differs between people with PD and healthy
ones. This paper aims to analyze differences between handmade drawings of PD patients and healthy subjects by applying
the SqueezeNet convolutional neural network (CNN) model as a feature extractor, and a support vector machine (SVM)
as a classifier. The dataset used for training and testing consists of 514 handwritten draws of Archimedes’ spiral images
derived from heterogeneous sources (digital and paper-based), from which 296 correspond to PD patients and 218 to healthy
subjects. To extract features using the proposed CNN, a model is trained and 20% of its data is used for testing. Feature
extraction results in 512 features, which are used for SVM training and testing, while the performance is compared with
that of other machine learning classifiers such as a Gaussian naive Bayes (GNB) classifier (82.61%) and a random forest
(RF) (87.38%). The proposed method displays an accuracy of 91.26%, which represents an improvement when compared
to pure CNN-based models such as SqueezeNet (85.29%), VGG11 (87.25%), and ResNet (89.22%).
Keywords
Parkinson’s disease, spirography, convolutional neural network, deep learning