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Covid-19 Diagnosis by WE-SAJ (Record no. 1771)

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Personal name Wang, Wei
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Title Covid-19 Diagnosis by WE-SAJ
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Date of publication, distribution, etc. 2022-12-31.
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General note /pmc/articles/PMC7613983/
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General note /pubmed/36568847
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Summary, etc. With a global COVID-19 pandemic, the number of confirmed patients increases rapidly, leaving the world with very few medical resources. Therefore, the fast diagnosis and monitoring of COVID-19 are one of the world's most critical challenges today. Artificial intelligence-based CT image classification models can quickly and accurately distinguish infected patients from healthy populations. Our research proposes a deep learning model (WE-SAJ) using wavelet entropy for feature extraction, two-layer FNNs for classification and the adaptive Jaya algorithm as a training algorithm. It achieves superior performance compared to the Jaya-based model. The model has a sensitivity of 85.47±1.84, specificity of 87.23±1.67 precision of 87.03±1.34, an accuracy of 86.35±0.70, and F1 score of 86.23±0.77, Matthews correlation coefficient of 72.75±1.38, and Fowlkes-Mallows Index of 86.24±0.76. Our experiments demonstrate the potential of artificial intelligence techniques for COVID-19 diagnosis and the effectiveness of the Self-adaptive Jaya algorithm compared to the Jaya algorithm for medical image classification tasks.
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Language note en
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Personal name Zhang, Xin
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9 (RLIN) 1803
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Personal name Wang, Shui-Hua
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Personal name Zhang, Yu-Dong
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Note Syst Sci Control Eng
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Uniform Resource Identifier <a href="http://dx.doi.org/10.1080/21642583.2022.2045645">http://dx.doi.org/10.1080/21642583.2022.2045645</a>
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