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Title: | Automatic Detection of Acute Leukemia (ALL and AML) Utilizing Customized Deep Graph Convolutional Neural Networks |
Authors: | Zare, L Rahmani, M Khaleghi, N Sheykhivand, S Danishvar, S |
Keywords: | ALL;AML;deep learning networks;leukemia;graph |
Issue Date: | 24-Jun-2024 |
Publisher: | MDPI |
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. |
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. |
Description: | Data Availability Statement: The data are private and the University Ethics Committee does not allow public access to the data. |
URI: | https://bura.brunel.ac.uk/handle/2438/29705 |
DOI: | https://doi.org/10.3390/bioengineering11070644 |
Other Identifiers: | ORCiD: Sobhan Sheykhivand https://orcid.org/0000-0002-2275-8133 ORCiD: Sebelan Danishvar https://orcid.org/0000-0002-8258-0437 644 |
Appears in Collections: | Dept of Computer Science Research Papers Dept of Civil and Environmental Engineering Research Papers |
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