000 | 01736 am a22002293u 4500 | ||
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042 | _adc | ||
100 | 1 | 0 |
_aWang, Wei _eauthor |
700 | 1 | 0 |
_aZhang, Xin _eauthor _91803 |
700 | 1 | 0 |
_aWang, Shui-Hua _eauthor |
700 | 1 | 0 |
_aZhang, Yu-Dong _eauthor |
245 | 0 | 0 | _aCovid-19 Diagnosis by WE-SAJ |
260 | _c2022-12-31. | ||
500 | _a/pmc/articles/PMC7613983/ | ||
500 | _a/pubmed/36568847 | ||
520 | _aWith 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. | ||
540 | _a | ||
546 | _aen | ||
690 | _aArticle | ||
655 | 7 |
_aText _2local |
|
786 | 0 | _nSyst Sci Control Eng | |
856 | 4 | 1 |
_uhttp://dx.doi.org/10.1080/21642583.2022.2045645 _zConnect to this object online. |
999 |
_c2050 _d2050 |