A Power Efficient Solution to Determine Red Blood Cell Deformation Type Using Binarized DenseNet

Reza, MT; Dipto, SM; Parvez, MZ; Barua, PD; Chakraborty, S

A Power Efficient Solution to Determine Red Blood Cell Deformation Type Using Binarized DenseNet

Reza, MT Dipto, SM Parvez, MZ Barua, PD Chakraborty, S

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23), 2023, 700 LNNS, pp. 246-256
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Reza, MT
dc.contributor.author	Dipto, SM
dc.contributor.author	Parvez, MZ
dc.contributor.author	Barua, PD
dc.contributor.author	Chakraborty, S https://orcid.org/0000-0002-0102-5424
dc.date	2023-05-08
dc.date.accessioned	2023-09-21T22:44:46Z
dc.date.available	2023-09-21T22:44:46Z
dc.date.issued	2023-01-01
dc.identifier.citation	Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23), 2023, 700 LNNS, pp. 246-256
dc.identifier.isbn	9783031337420
dc.identifier.issn	2367-3370
dc.identifier.issn	2367-3389
dc.identifier.uri	http://hdl.handle.net/10453/172243
dc.description.abstract	Red Blood Cells (RBCs) play an important role in the welfare of human being as it helps to transport oxygen throughout the body. Different RBC-related diseases, for example, variants of anemias, can disrupt regular functionality and become life-threatening. Classification systems leveraging CNNs can be useful for automated diagnosis of RBC deformation, but the system can be quite resource-intensive in case the CNN architecture is large. The proposed approach provides an empirical analysis of the application of 28 and 45-layer Binarized DenseNet for identifying RBC deformations. According to our investigation, the accuracy of the 45-layer binarized variant can reach 93–94%, which is on par with the results of the conventional variant, which also achieves 93–94% accuracy. The 23-layer binarized variant, while not on par with the regular variant, also gets very close in terms of accuracy. Meanwhile, the 45-layer and 28-layer binarized variant only requires 9% and 11% storage space respectively to that of regular DenseNet, with potentially faster inference time. This optimized model can be useful since it can be easily deployed in resource-constrained devices, such as mobile phones and cheap embedded systems.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23)
dc.relation.ispartof	International Conference on Advances in Computing Research
dc.relation.ispartofseries	Lecture Notes in Networks and Systems
dc.relation.isbasedon	10.1007/978-3-031-33743-7_21
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A Power Efficient Solution to Determine Red Blood Cell Deformation Type Using Binarized DenseNet
dc.type	Conference Proceeding
utslib.citation.volume	700 LNNS
utslib.location.activity	Orlando, Florida
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	*
pubs.consider-herdc	false
dc.date.updated	2023-09-21T22:44:45Z
pubs.finish-date	2023-05-10
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.start-date	2023-05-08
pubs.volume	700 LNNS
dc.location	Switzerland

Abstract:

Red Blood Cells (RBCs) play an important role in the welfare of human being as it helps to transport oxygen throughout the body. Different RBC-related diseases, for example, variants of anemias, can disrupt regular functionality and become life-threatening. Classification systems leveraging CNNs can be useful for automated diagnosis of RBC deformation, but the system can be quite resource-intensive in case the CNN architecture is large. The proposed approach provides an empirical analysis of the application of 28 and 45-layer Binarized DenseNet for identifying RBC deformations. According to our investigation, the accuracy of the 45-layer binarized variant can reach 93–94%, which is on par with the results of the conventional variant, which also achieves 93–94% accuracy. The 23-layer binarized variant, while not on par with the regular variant, also gets very close in terms of accuracy. Meanwhile, the 45-layer and 28-layer binarized variant only requires 9% and 11% storage space respectively to that of regular DenseNet, with potentially faster inference time. This optimized model can be useful since it can be easily deployed in resource-constrained devices, such as mobile phones and cheap embedded systems.

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http://hdl.handle.net/10453/172243