Novel multiple pooling and local phase quantization stable feature extraction techniques for automated classification of brain infarcts

Dogan, S; Barua, PD; Baygin, M; Chakraborty, S; Ciaccio, EJ; Tuncer, T; Abd Kadir, KA; Md Shah, MN; Azman, RR; Lee, CC; Ng, KH; Acharya, UR

Novel multiple pooling and local phase quantization stable feature extraction techniques for automated classification of brain infarcts

Dogan, S Barua, PD Baygin, M Chakraborty, S

Ciaccio, EJ Tuncer, T Abd Kadir, KA Md Shah, MN Azman, RR Lee, CC Ng, KH Acharya, UR

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Biocybernetics and Biomedical Engineering, 2022, 42, (3), pp. 815-828
Issue Date:: 2022-07-01

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Field	Value	Language
dc.contributor.author	Dogan, S
dc.contributor.author	Barua, PD
dc.contributor.author	Baygin, M
dc.contributor.author	Chakraborty, S https://orcid.org/0000-0002-0102-5424
dc.contributor.author	Ciaccio, EJ
dc.contributor.author	Tuncer, T
dc.contributor.author	Abd Kadir, KA
dc.contributor.author	Md Shah, MN
dc.contributor.author	Azman, RR
dc.contributor.author	Lee, CC
dc.contributor.author	Ng, KH
dc.contributor.author	Acharya, UR
dc.date.accessioned	2023-03-27T03:43:04Z
dc.date.available	2023-03-27T03:43:04Z
dc.date.issued	2022-07-01
dc.identifier.citation	Biocybernetics and Biomedical Engineering, 2022, 42, (3), pp. 815-828
dc.identifier.issn	0208-5216
dc.identifier.uri	http://hdl.handle.net/10453/168572
dc.description.abstract	This study aims to introduce a hand-crafted machine learning method to classify ischemic and hemorrhagic strokes with satisfactory performance. In the first step of this work, a new CT brain for images dataset was collected for stroke patients. A highly accurate hand-crafted machine learning method is developed and tested for these cases. This model uses preprocessing, feature creation using a novel pooling method (it is named P9), a local phase quantization (LPQ) operator, and a Chi2-based selector responsible for selecting the most significant features. After that, classification is done using the k-nearest neighbor (kNN) classifier with ten-fold cross-validation (CV). The novel aspect of this model is the P9 pooling method. The inspiration for this pooling method was drawn from the deep learning models, where features are extracted with multiple layers using a convolution operator applied to the pooling method. However, pooling decompositions have a routing problem. The P9 pooling function creates nine decomposed models, hence the name. The LPQ feature extractor is applied to images to generate sub-bands for feature generation. The Chi2 selector is then employed to select the most significant features from the created feature vector, and these features are utilized for the classification using the k-nearest neighbor algorithm (kNN). The introduced P9-LPQ feature extraction-based learning model attained over 98% classification accuracy in all cases. The results obtained in this paper show that the proposed method can successfully classify stroke types. For this reason, the developed model can pre-diagnose stroke types in the future.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Biocybernetics and Biomedical Engineering
dc.relation.isbasedon	10.1016/j.bbe.2022.06.004
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Novel multiple pooling and local phase quantization stable feature extraction techniques for automated classification of brain infarcts
dc.type	Journal Article
utslib.citation.volume	42
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-27T03:43:03Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	42
utslib.citation.issue	3

Abstract:

This study aims to introduce a hand-crafted machine learning method to classify ischemic and hemorrhagic strokes with satisfactory performance. In the first step of this work, a new CT brain for images dataset was collected for stroke patients. A highly accurate hand-crafted machine learning method is developed and tested for these cases. This model uses preprocessing, feature creation using a novel pooling method (it is named P9), a local phase quantization (LPQ) operator, and a Chi2-based selector responsible for selecting the most significant features. After that, classification is done using the k-nearest neighbor (kNN) classifier with ten-fold cross-validation (CV). The novel aspect of this model is the P9 pooling method. The inspiration for this pooling method was drawn from the deep learning models, where features are extracted with multiple layers using a convolution operator applied to the pooling method. However, pooling decompositions have a routing problem. The P9 pooling function creates nine decomposed models, hence the name. The LPQ feature extractor is applied to images to generate sub-bands for feature generation. The Chi2 selector is then employed to select the most significant features from the created feature vector, and these features are utilized for the classification using the k-nearest neighbor algorithm (kNN). The introduced P9-LPQ feature extraction-based learning model attained over 98% classification accuracy in all cases. The results obtained in this paper show that the proposed method can successfully classify stroke types. For this reason, the developed model can pre-diagnose stroke types in the future.

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http://hdl.handle.net/10453/168572