A hybrid two-stage SqueezeNet and support vector machine system for Parkinson’s disease detection based on handwritten spiral patterns

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%).

Hand-Sketchs based Parkinson's disease Screening using Lightweight Deep-Learning with Two-Fold Training and Fused Optimal Features

“…Recently, machine-learning (ML) and deep learning (DL) based procedures have been widely performed by researchers to automate hand-sketch examination procedures [6][7][8][9][10][11]. The earlier works confirm that the implemented ML and DL schemes efficiently segregate the considered hand sketches into healthy/PD classes [12,13].…”

Assessment measures of an ensemble classifier based on the distributivity equation to predict the presence of severe coronary artery disease

“…Each of these techniques has its strengths and weaknesses. These methods have been widely investigated in broad areas such as medical applications (e.g., breast cancer (Kowal et al, 2021), Parkinson's disease (Bernardo et al, 2021) or electrocardiogram (ECG) signals of the human heart (Patro et al, 2022)) for purposes such as screening, risk stratification, prediction, and assisted decision-making (Castaneda et al, 2015).…”

Modified SqueezeNet Architecture for Parkinson’s Disease Detection Based on Keypress Data

“…A hybrid two-stage approach for detecting PD was proposed in [ 26 ]. The proposed solution counted 514 spiral drawings made by PD-affected and healthy individuals.…”