Novel nested patch-based feature extraction model for automated Parkinson’s Disease symptom classification using MRI images
Article
Article Title | Novel nested patch-based feature extraction model for automated Parkinson’s Disease symptom classification using MRI images |
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ERA Journal ID | 5039 |
Article Category | Article |
Authors | Kaplan, Ela (Author), Altunisik, Erman (Author), Firat, Yasemin Ekmekyapar (Author), Barua, Prabal Datta (Author), Dogan, Sengul (Author), Baygin, Mehmet (Author), Demir, Fahrettin Burak (Author), Tuncer, Turker (Author), Palmer, Elizabeth (Author), Tan, Ru-San (Author), Yu, Ping (Author), Soar, Jeffrey (Author), Fujita, Hamido (Author) and Acharya, U. Rajendra (Author) |
Journal Title | Computer Methods and Programs in Biomedicine |
Journal Citation | 224, pp. 1-11 |
Article Number | 107030 |
Number of Pages | 11 |
Year | 2022 |
Publisher | Elsevier |
Place of Publication | Ireland |
ISSN | 0169-2607 |
1872-7565 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.cmpb.2022.107030 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0169260722004126 |
Abstract | Objective: Parkinson's disease (PD) is a common neurological disorder with variable clinical manifestations and magnetic resonance imaging (MRI) findings. We propose an effective, handcrafted image classification model that can accurately (i) classify different PD stages, (ii) detect comorbid dementia, and (iii) discriminate PD-related motor symptoms. Methods: Selected image datasets from three PD studies were used to develop the classification model. Our proposed novel automated system was developed in four phases: (i) texture features are extracted from the non-fixed size patches. In the feature extraction phase, a pyramid histogram-oriented gradient (PHOG) image descriptor is used. (ii) In the feature selection phase, four feature selectors: neighborhood component analysis (NCA), Chi2, minimum redundancy maximum relevancy (mRMR), and ReliefF are used to generate four feature vectors. (iii) Two classifiers: k-nearest neighbor (kNN) and support vector machine (SVM) are used in the classification step. A ten-fold cross-validation technique is used to validate the results. (iv) Eight predicted vectors are generated using four selected feature vectors and two classifiers. Finally, iterative majority voting (IMV) is used to attain general classification results. Therefore, this model is named nested patch-PHOG-multiple feature selectors and multiple classifiers-IMV (NP-PHOG-MFSMCIMV). Results: Our presented NP-PHOG-MFSMCIMV model achieved 99.22%, 98.70%, and 99.53% accuracies for the collected PD stages, PD dementia, and PD symptoms classification datasets, respectively. Significance: The obtained accuracies (over 98% for all states) demonstrated the performance of developed NP-PHOG-MFSMCIMV model in automated PD state classification. |
Keywords | PD image classification; Nested patch division; Local binary pattern; Local phase quantization; Neighborhood component analysis; Image classification |
ANZSRC Field of Research 2020 | 460902. Decision support and group support systems |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Institution of Origin | University of Southern Queensland |
Byline Affiliations | Adıyaman Training and Research Hospital, Turkiye |
Adiyaman University, Turkiye | |
Sanko University, Turkiye | |
School of Business | |
Firat University, Turkey | |
Ardahan University, Turkiye | |
Bandirma Onyedi Eylul University, Turkiye | |
Department of Health, New South Wales | |
National Heart Centre, Singapore | |
University of Wollongong | |
HUTECH University of Technology, Vietnam | |
School of Mathematics, Physics and Computing | |
Institute for Life Sciences and the Environment | |
Centre for Health Research | |
Institute for Life Sciences and the Environment | |
Kumamoto University, Japan |
https://research.usq.edu.au/item/q78w1/novel-nested-patch-based-feature-extraction-model-for-automated-parkinson-s-disease-symptom-classification-using-mri-images
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