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

• Autorzy: Bernardo Lucas Salvador , Damaševičius Robertas , de Albuquerque Victor Hugo C. , Maskeliūnas Rytis

Identyfikatory

DOI

10.34768/amcs-2021-0037

• Języki publikacji: EN

Abstrakty

EN

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%).

Słowa kluczowe

EN

Parkinson’s disease; spirography; convolutional neural network; deep learning

PL

choroba Parkinsona; spirografia; sieć neuronowa konwolucyjna; uczenie głębokie

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2021
• Tom: Vol. 31, no. 4
• Strony: 549--561
• Opis fizyczny: Bibliogr. 66 poz., rys., tab.

Twórcy

autor

Bernardo Lucas Salvador

• Department of Software Engineering, Kaunas University of Technology, Studentu 50, Kaunas 51368, Lithuania

autor

Damaševičius Robertas

• Department of Software Engineering, Kaunas University of Technology, Studentu 50, Kaunas 51368, Lithuania

autor

de Albuquerque Victor Hugo C.

• Department of Teleinformatics Engineering, Federal University of Ceara, Campus do Pici, Fortaleza 60811-341, Brazil

autor

Maskeliūnas Rytis

• Department of Multimedia Engineering, Kaunas University of Technology, K. Baršausko 59, Kaunas 51423, Lithuania

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).