ConcatNeXt: An automated blood cell classification with a new deep convolutional neural network

Erten, M; Barua, PD; Dogan, S; Tuncer, T; Tan, RS; Acharya, UR

ConcatNeXt: An automated blood cell classification with a new deep convolutional neural network

Erten, M Barua, PD Dogan, S Tuncer, T Tan, RS Acharya, UR

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Publisher:: Springer Nature
Publication Type:: Journal Article
Citation:: Multimedia Tools and Applications, 2025, 84, (20), pp. 22231-22249
Issue Date:: 2025-01-01

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Field	Value	Language
dc.contributor.author	Erten, M
dc.contributor.author	Barua, PD
dc.contributor.author	Dogan, S
dc.contributor.author	Tuncer, T
dc.contributor.author	Tan, RS
dc.contributor.author	Acharya, UR
dc.date.accessioned	2025-07-15T05:22:47Z
dc.date.available	2025-07-15T05:22:47Z
dc.date.issued	2025-01-01
dc.identifier.citation	Multimedia Tools and Applications, 2025, 84, (20), pp. 22231-22249
dc.identifier.issn	1380-7501
dc.identifier.issn	1573-7721
dc.identifier.uri	http://hdl.handle.net/10453/188207
dc.description.abstract	Examining peripheral blood smears is valuable in clinical settings, yet manual identification of blood cells proves time-consuming. To address this, an automated blood cell image classification system is crucial. Our objective is to develop a precise automated model for detecting various blood cell types, leveraging a novel deep learning architecture. We harnessed a publicly available dataset of 17,092 blood cell images categorized into eight classes. Our innovation lies in ConcatNeXt, a new convolutional neural network. In the spirit of Geoffrey Hinton's approach, we adapted ConvNeXt by substituting the Gaussian error linear unit with a rectified linear unit and layer normalization with batch normalization. We introduced depth concatenation blocks to fuse information effectively and incorporated a patchify layer. Integrating ConcatNeXt with nested patch-based deep feature engineering, featuring downstream iterative neighborhood component analysis and support vector machine-based functions, establishes a comprehensive approach. ConcatNeXt achieved notable validation and test accuracies of 97.43% and 97.77%, respectively. The ConcatNeXt-based feature engineering model further elevated accuracy to 98.73%. Gradient-weighted class activation maps were employed to provide interpretability, offering valuable insights into model decision-making. Our proposed ConcatNeXt and nested patch-based deep feature engineering models excel in blood cell image classification, showcasing remarkable classification performances. These innovations mark significant strides in computer vision-based blood cell analysis.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Multimedia Tools and Applications
dc.relation.isbasedon	10.1007/s11042-024-19899-x
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0803 Computer Software, 0805 Distributed Computing, 0806 Information Systems
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	Software Engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.subject.classification	4603 Computer vision and multimedia computation
dc.subject.classification	4605 Data management and data science
dc.subject.classification	4606 Distributed computing and systems software
dc.title	ConcatNeXt: An automated blood cell classification with a new deep convolutional neural network
dc.type	Journal Article
utslib.citation.volume	84
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
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	2025-07-15T05:22:45Z
pubs.issue	20
pubs.publication-status	Published
pubs.volume	84
utslib.citation.issue	20

Abstract:

Examining peripheral blood smears is valuable in clinical settings, yet manual identification of blood cells proves time-consuming. To address this, an automated blood cell image classification system is crucial. Our objective is to develop a precise automated model for detecting various blood cell types, leveraging a novel deep learning architecture. We harnessed a publicly available dataset of 17,092 blood cell images categorized into eight classes. Our innovation lies in ConcatNeXt, a new convolutional neural network. In the spirit of Geoffrey Hinton's approach, we adapted ConvNeXt by substituting the Gaussian error linear unit with a rectified linear unit and layer normalization with batch normalization. We introduced depth concatenation blocks to fuse information effectively and incorporated a patchify layer. Integrating ConcatNeXt with nested patch-based deep feature engineering, featuring downstream iterative neighborhood component analysis and support vector machine-based functions, establishes a comprehensive approach. ConcatNeXt achieved notable validation and test accuracies of 97.43% and 97.77%, respectively. The ConcatNeXt-based feature engineering model further elevated accuracy to 98.73%. Gradient-weighted class activation maps were employed to provide interpretability, offering valuable insights into model decision-making. Our proposed ConcatNeXt and nested patch-based deep feature engineering models excel in blood cell image classification, showcasing remarkable classification performances. These innovations mark significant strides in computer vision-based blood cell analysis.

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http://hdl.handle.net/10453/188207