000			01752 am a22002293u 4500
042			_adc
100	1	0	_aWang, Wei _eauthor _91802
700	1	0	_aZhang, Xin _eauthor _91803
700	1	0	_aWang, Shui-Hua _eauthor _91804
700	1	0	_aZhang, Yu-Dong _eauthor _91805
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			_c347 _d347