FlexiCombFE: A flexible, combination-based feature engineering framework for brain tumor detection

Tuncer, I; Baig, AH; Barua, PD; Hajiyeva, R; Massimo, S; Dogan, S; Tuncer, T; Acharya, UR

FlexiCombFE: A flexible, combination-based feature engineering framework for brain tumor detection

Tuncer, I Baig, AH Barua, PD Hajiyeva, R Massimo, S Dogan, S Tuncer, T Acharya, UR

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Biomedical Signal Processing and Control, 2025, 104, pp. 107538
Issue Date:: 2025-06-01

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Field	Value	Language
dc.contributor.author	Tuncer, I
dc.contributor.author	Baig, AH
dc.contributor.author	Barua, PD
dc.contributor.author	Hajiyeva, R
dc.contributor.author	Massimo, S
dc.contributor.author	Dogan, S
dc.contributor.author	Tuncer, T
dc.contributor.author	Acharya, UR
dc.date.accessioned	2025-08-06T04:03:44Z
dc.date.available	2025-08-06T04:03:44Z
dc.date.issued	2025-06-01
dc.identifier.citation	Biomedical Signal Processing and Control, 2025, 104, pp. 107538
dc.identifier.issn	1746-8094
dc.identifier.issn	1746-8108
dc.identifier.uri	http://hdl.handle.net/10453/189160
dc.description.abstract	Deep learning models are prevalently employed for image classification, but significant opportunities remain within the domain of feature engineering. This study introduces FlexiCombFE, a novel, flexible, combination-based feature engineering framework for brain tumor detection using various fixed-size patch divisions. This framework employs three distinct feature extractors (Local Phase Quantization, Local Binary Pattern, and Pyramidal Histogram of Oriented Gradients) to generate a total of seven primary feature vectors. By using four types of fixed-size patch divisions, 28 feature vectors are generated. Then, three feature selectors (Chi-squared, Neighborhood Component Analysis, and ReliefF) create 84 selected feature vectors. In the classification phase K-nearest neighbors and support vector machine classifiers yield 168 classifier-specific outcomes. An information fusion generates 166-voted outcomes, with the most accurate classification outcome selected as the final output. This self-organizing feature engineering model achieved a classification accuracy of 99.35% on a brain tumor image dataset, outperforming several deep learning approaches. The modular design of the proposed framework allows for detailed analysis of the classification effects of individual methods, providing optimal feature engineering strategies for medical image classification tasks.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Biomedical Signal Processing and Control
dc.relation.isbasedon	10.1016/j.bspc.2025.107538
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0903 Biomedical Engineering, 0906 Electrical and Electronic Engineering, 1004 Medical Biotechnology
dc.subject.classification	Biomedical Engineering
dc.subject.classification	3006 Food sciences
dc.subject.classification	4003 Biomedical engineering
dc.title	FlexiCombFE: A flexible, combination-based feature engineering framework for brain tumor detection
dc.type	Journal Article
utslib.citation.volume	104
utslib.for	0903 Biomedical Engineering
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1004 Medical Biotechnology
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 Biomedical Engineering
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-08-06T04:03:40Z
pubs.publication-status	Published
pubs.volume	104

Abstract:

Deep learning models are prevalently employed for image classification, but significant opportunities remain within the domain of feature engineering. This study introduces FlexiCombFE, a novel, flexible, combination-based feature engineering framework for brain tumor detection using various fixed-size patch divisions. This framework employs three distinct feature extractors (Local Phase Quantization, Local Binary Pattern, and Pyramidal Histogram of Oriented Gradients) to generate a total of seven primary feature vectors. By using four types of fixed-size patch divisions, 28 feature vectors are generated. Then, three feature selectors (Chi-squared, Neighborhood Component Analysis, and ReliefF) create 84 selected feature vectors. In the classification phase K-nearest neighbors and support vector machine classifiers yield 168 classifier-specific outcomes. An information fusion generates 166-voted outcomes, with the most accurate classification outcome selected as the final output. This self-organizing feature engineering model achieved a classification accuracy of 99.35% on a brain tumor image dataset, outperforming several deep learning approaches. The modular design of the proposed framework allows for detailed analysis of the classification effects of individual methods, providing optimal feature engineering strategies for medical image classification tasks.

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http://hdl.handle.net/10453/189160