Repozytorium Uniwersytetu Zielonogórskiego udostępnia 9 272 obiektów cyfrowych
Obiekt
Tytuł: A hybrid two-stage SqueezeNet and support vector machine system for Parkinson`s disease detection based on handwritten spiral patterns
Autor:
Bernardo, Lucas Salvador ; Damaševičius, Robertas ; Albuquerque, Victor Hugo C. de ; Maskeliunas, Rytis
Data wydania:
Typ zasobu:
Współtwórca:
Kusy, Maciej - ed. ; Scherer, Rafał - ed. ; Krzyżak, Adam - ed.
Podtytuł:
Tytuł publikacji grupowej:
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%).
Wydawca:
Zielona Góra: Uniwersytet Zielonogórski
Identyfikator zasobu:
DOI:
Strony:
Źródło:
AMCS, volume 31, number 4 (2021) ; kliknij tutaj, żeby przejść
Jezyk:
Licencja CC BY 4.0:
Prawa do dysponowania publikacją:
Biblioteka Uniwersytetu Zielonogórskiego
Kolekcje, do których przypisany jest obiekt:
- Repozytorium > Jednostki organizacyjne > Wydział Informatyki, Elektrotechniki i Automatyki
- Repozytorium > Typy utworów > Artykuły
- Repozytorium > Czasopisma naukowe i serie wydawnicze UZ > International Journal of Applied Mathematics and Computer Science (AMCS) > International Journal of Applied Mathematics and Computer Science (AMCS) (2021)
Data ostatniej modyfikacji:
21 lip 2025
Data dodania obiektu:
21 lip 2025
Liczba wyświetleń treści obiektu:
12
Wszystkie dostępne wersje tego obiektu:
https://zbc.uz.zgora.pl/repozytorium/publication/101389
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 31, number 1 (2021)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 31, number 2 (2021)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 31, number 3 (2021)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 31, number 4 (2021)
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