Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Anusha, B.a | Geetha, P.a | Kannan, A.b
Affiliations: [a] Department of Information Science and Technology, Anna University, Chennai, Tamil Nadu, India | [b] School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
Correspondence: [*] Corresponding author. B. Anusha, Ph.D., Research Scholar, Information Science and Technology, CEG campus, Anna University, Chennai-600025, Tamil Nadu, India. E-mail: anushabphd@gmail.com.
Abstract: The identification of Parkinson’s Disease (PD) is a necessary concern for reducing the occurrences of nervous disorders and brain death. The prediction of PD based on symptoms is depending on the body conditions of patients as the symptoms differ for every individual. Doctors preferably use ionized radiation-free MRI scans since they offer more precise images of soft tissues in the brain. In the recent years, deep learning is the prominently used method for performing image analysis and classification. However, the systems developed using deep learning are not able to predict the PD accurately. In order to bridge the gaps present in the existing systems, we propose a hybrid model based on neuro-fuzzy classification to detect PD more accurately. For enhancing the accuracy of PD identification, we used the ResNet-18 deep learning architecture for the classification of MRI images. In addition to this, a hybrid framework is also proposed in this paper where the softmax layer of ResNet-18 is modified using non-linear SVM and Fuzzy SVM (fSVM) classifiers. The convolution and max-pooling layers of ResNet-18 are able to learn more objective features for classification. The proposed hybrid model of ResNet-fSVM is evaluated on the neuro-MRI images from the PPMI dataset and achieved 4.4% higher accuracy than the ResNet-18 model and 2.8% higher accuracy than hybrid ResNet-SVM model. The age group based results obtained in this work has proved that the accuracy of the proposed ResNet-fSVM hybrid model is better when it is compared with ResNet-18 and hybrid ResNet-SVM models. This system effectively detects Early-onset PD through its efficiency in classification.
Keywords: Parkinson’s disease (PD), magnetic resonance imaging (MRI), pre-trained ResNet-18, support vector machine, fuzzy support vector machine
DOI: 10.3233/JIFS-220271
Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2711-2729, 2022
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl