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http://bura.brunel.ac.uk/handle/2438/29705
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DC Field | Value | Language |
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dc.contributor.author | Zare, L | - |
dc.contributor.author | Rahmani, M | - |
dc.contributor.author | Khaleghi, N | - |
dc.contributor.author | Sheykhivand, S | - |
dc.contributor.author | Danishvar, S | - |
dc.date.accessioned | 2024-09-11T14:00:53Z | - |
dc.date.available | 2024-09-11T14:00:53Z | - |
dc.date.issued | 2024-06-24 | - |
dc.identifier | ORCiD: Sobhan Sheykhivand https://orcid.org/0000-0002-2275-8133 | - |
dc.identifier | ORCiD: Sebelan Danishvar https://orcid.org/0000-0002-8258-0437 | - |
dc.identifier | 644 | - |
dc.identifier.citation | Zare, L. et al. (2024) 'Automatic Detection of Acute Leukemia (ALL and AML) Utilizing Customized Deep Graph Convolutional Neural Networks', Bioengineering, 11 (7), 644, pp. 1 - 19. doi: 10.3390/bioengineering11070644. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/29705 | - |
dc.description | Data Availability Statement: The data are private and the University Ethics Committee does not allow public access to the data. | en_US |
dc.description.abstract | Leukemia is a malignant disease that impacts explicitly the blood cells, leading to life-threatening infections and premature mortality. State-of-the-art machine-enabled technologies and sophisticated deep learning algorithms can assist clinicians in early-stage disease diagnosis. This study introduces an advanced end-to-end approach for the automated diagnosis of acute leukemia classes acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML). This study gathered a complete database of 44 patients, comprising 670 ALL and AML images. The proposed deep model’s architecture consisted of a fusion of graph theory and convolutional neural network (CNN), with six graph Conv layers and a Softmax layer. The proposed deep model achieved a classification accuracy of 99% and a kappa coefficient of 0.85 for ALL and AML classes. The suggested model was assessed in noisy conditions and demonstrated strong resilience. Specifically, the model’s accuracy remained above 90%, even at a signal-to-noise ratio (SNR) of 0 dB. The proposed approach was evaluated against contemporary methodologies and research, demonstrating encouraging outcomes. According to this, the suggested deep model can serve as a tool for clinicians to identify specific forms of acute leukemia. | en_US |
dc.description.sponsorship | This research received no external funding. | en_US |
dc.format.medium | Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | MDPI | en_US |
dc.rights | Copyright © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | ALL | en_US |
dc.subject | AML | en_US |
dc.subject | deep learning networks | en_US |
dc.subject | leukemia | en_US |
dc.subject | graph | en_US |
dc.title | Automatic Detection of Acute Leukemia (ALL and AML) Utilizing Customized Deep Graph Convolutional Neural Networks | en_US |
dc.type | Article | en_US |
dc.date.dateAccepted | 2024-06-21 | - |
dc.identifier.doi | https://doi.org/10.3390/bioengineering11070644 | - |
dc.relation.isPartOf | Bioengineering | - |
pubs.issue | 7 | - |
pubs.publication-status | Published | - |
pubs.volume | 11 | - |
dc.identifier.eissn | 2306-5354 | - |
dc.rights.license | https://creativecommons.org/licenses/by/4.0/legalcode.en | - |
dc.rights.holder | The authors | - |
Appears in Collections: | Dept of Computer Science Research Papers Dept of Civil and Environmental Engineering Research Papers |
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FullText.pdf | Copyright © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | 3.41 MB | Adobe PDF | View/Open |
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