[1] Wang Wenling ,Xu Yanli ,Gao Ruqin ,et al. Detection of SARS-CoV-2 in different types of clinical specimens[J]. Jama ,2020 ,323(18) : 1843- 1844.
[2] Kucirka Lauren M. ,Lauer Stephen A. ,Laeyendecker Oli- ver ,et al. Variation in false -negative rate of reverse tran- scriptase polymerase chain reaction - based SARS- CoV- 2 tests by time since exposure[J]. Annals of internal medi- cine ,2020 ,173(4) : 262-267.
[3] Abbas Asmaa ,Abdelsamea Mohammed M. ,Gaber Moham- ed Medhat. Classification of COVID- 19 in chest X-ray im- ages using DeTraC deep convolutional neural network[J]. Applied Intelligence ,2021 ,51(2) : 854- 864.
[4] Sethy Prabira Kumar ,Behear Santi Kumari ,Ratha Prady- umna Kumar ,et al. Detection of coronavirus disease ( covid- 19 ) based on deep features[J]. Preprints 2020 ,2020030300.
[5] Ohata Elene Firmeza ,Bezerra Gabriel Maia ,Victor Joao ,et al. Automatic detection of COVID - 19 infection using chest X-ray images through transfer learning[J]. IEEE/ CAA Journal of Automatica Sinica ,2020 ,8 ( 1 ) : 239 - 248.
[6] Shome Debaditya ,Kar T ,Mohanty Sachi Nandan ,et al. Covid-transformer : Interpretable covid - 19 detection using vision transformer for healthcare[J]. International Journalof Environmental Research and Public Health 2021 18(21) : 11086.
[7] Rahman Tawsifur ,Khandakar Amith ,Qiblawey Yazan ,et al. Exploring the effect of image enhancement techniques on COVID- 19 detection using chest X-ray images[J]. Com- puters in biology and medicine ,2021 ,132 : 104319.
[8] Ramachandran Prajit ,Parmar Niki ,Vaswani Ashish ,et al. Stand-alone self-attention in vision models[J]. Advances in Neural Information Processing Systems ,2019 ,32 :1045- 1054.
[9] Han Kai ,Xiao An ,Wu Enhua ,et al. Transformer in trans- former[J]. Advances in Neural Information Processing Sys- tems ,2021 ,34 : 15908- 15919.
[10] Wang Wenhai ,Xie Enze ,Li Xiang ,et al. Pyramid vision transformer : A versatile backbone for dense prediction with- out convolutions[ C]//Proceedings of the IEEE/CVF Inter- national Conference on Computer Vision. Montreal : IEEE Press ,2021 : 568-578.
[11] Wang Wenhai ,Xie Enze ,Li Xiang ,et al. Pvt v2 : Im- proved baselines with pyramid vision transformer[J]. Com- putational Visual Media ,2022 ,8(3) : 415-424.
[12] Liu Ze ,Lin Yutong ,Cao Yue ,et al. Swin transformer : Hi- erarchical vision transformer using shifted windows [ C]// Proceedings of the IEEE/CVF International Conference on Computer Vision. Montreal : IEEE Press ,2021 : 10012 - 10022.
[13] Gu Yu ,Lu Xaingqi ,Yang Lidong ,et al. Automatic lung nodule detection using a 3D deep convolutional neural net-work combined with a multi - scale prediction strategy in chest CTs[J]. Computers in biology and medicine ,2018 ,103 : 220-231.
[14] Chowdhury Muhammad Eh ,Rahman Tawsifur ,Khanadakar Amith ,et al. Can AI help in screening viral and COVID-19 pneumonia[J]. IEEE Access ,2020 ,8 : 132665 - 132676.
[15] Mdli Vayá ,Jm Saborit ,Ja Montell ,et al. Bimcv covid- 19 + : a large annotated dataset of rx and ct images from covid-19 patients[J]. arXiv preprint arXiv : 2006 ( 01174 ) ,2020.
[16] Chen Ruchong ,Liang Wenhua ,Jiang Mei ,et al. Risk fac- tors of fatal outcome in hospitalized subjects with coronavir- us disease 2019 from a nationwide analysis in China[J]. Chest ,2020 ,158(1) : 97- 105.
[17] Weng Zhihong ,Chen Qiaosen ,Li Sumeng ,et al. ANDC : an early warning score to predict mortality risk for patients with Coronavirus Disease 2019[J]. Journal of translational medicine ,2020 ,18(1) : 1- 10.
[18] Liu Jingyuan ,Liu Yao ,Pan Xiang ,et al. Neutrophil-to - lymphocyte ratio predicts severe illness patients with 2019 novel coronavirus in the early stage[J]. MedRxiv ,2020 ,56 :1110- 1120.
[19] Huang Ian ,Pranata Raymond ,Lymphopenia in severe coro- navirus disease-2019 ( COVID- 19) : systematic review and meta-analysis[J]. Journal of intensive care ,2020 ,8(1) : 1- 10.
[20] Lin Tsung-Chieh ,Lee Hsi-Chieh ,Covid- 19 chest radio- graphy images analysis based on integration of image prepro- cess ,guided grad- CAM ,machine learning and risk man- agement[ C]//Proceedings of the 4th International Confer- ence on Medical and Health Informatics. Kyoto : IEEE Press ,2020 : 281-288.
[21] Gu Yu ,Chi Jingqian ,Liu Jiaqi ,et al. A survey of comput- er - aided diagnosis of lung nodules from CT scans using deep learning[J]. Computers in biology and medicine ,2021 ,137 : 104806.
