[1] ZHOU T, LIU F Z, LU H L et al. A review of deep learning imaging diagnostic methods for COVID-19[J]. Electronics, 12, 1167(2023).

[2] TORRES-RAMIREZ C A, TIMARAN-MONTENEGRO D, MATEO-CAMACHO Y S et al. CT-based pathological lung opacities volume as a predictor of critical illness and inflammatory response severity in patients with COVID-19[J]. Heliyon, 8(2022).

[3] REN K, GU Y, LUO M S et al. Deep-learning-based denoising of X-ray differential phase and dark-field images[J]. European Journal of Radiology, 163, 110835(2023).

[4] MAMALAKIS M, SWIFT A J, VORSELAARS B et al. DenResCov-19： a deep transfer learning network for robust automatic classification of COVID-19， pneumonia， and tuberculosis from X-rays[J]. Computerized Medical Imaging and Graphics, 94, 102008(2021).

[5] HE K M, ZHANG X Y, REN S Q et al. Deep residual learning for image recognition[C], 27, 770-778(2016).

[6] ZHOU T, CHANG X Y, LIU Y C et al. COVID-ResNet： COVID-19 recognition based on improved attention ResNet[J]. Electronics, 12, 1413(2023).

[7] CHEN Y F, LIN Y L, XU X D et al. Classification of lungs infected COVID-19 images based on inception-ResNet[J]. Computer Methods and Programs in Biomedicine, 225, 107053(2022).

[8] HUANG Q H, LEI Y, XING W Y et al. Evaluation of pulmonary edema using ultrasound imaging in patients with COVID-19 pneumonia based on a non-local channel attention ResNet[J]. Ultrasound in Medicine & Biology, 48, 945-953(2022).

[9] AHILA T, SUBHAJINI A C. E-GCS： detection of COVID-19 through classification by attention bottleneck residual network[J]. Engineering Applications of Artificial Intelligence, 116, 105398(2022).

[10] CHEN B Z, LI J X, GUO X B et al. DualCheXNet： dual asymmetric feature learning for thoracic disease classification in chest X-rays[J]. Biomedical Signal Processing and Control, 53, 101554(2019).

[11] [11] 吴宣言， 缑新科， 朱子重， 等. 深层聚合残差密集网络的超声图像左心室分割［J］. 中国图象图形学报， 2020， 25（9）： 1930-1942. doi: 10.11834/jig.190552WUX Y， GOUX K， ZHUZ ZH， et al. Left ventricular segmentation on ultrasound images using deep layer aggregation for residual dense networks［J］. Journal of Image and Graphics， 2020， 25（9）： 1930-1942.（in Chinese）. doi: 10.11834/jig.190552

[12] [12] 李锵， 王旭， 关欣. 一种结合三重注意力机制的双路径网络胸片疾病分类方法［J］. 电子与信息学报， 2023， 45（4）： 1412-1425. doi: 10.11999/JEIT220172LIQ， WANGX， GUANX. A dual-path network chest film disease classification method combined with a triple attention mechanism［J］. Journal of Electronics & Information Technology， 2023， 45（4）： 1412-1425.（in Chinese）. doi: 10.11999/JEIT220172

[13] [13] 周涛， 刘赟璨， 陆惠玲， 等. ResNet及其在医学图像处理领域的应用：研究进展与挑战［J］. 电子与信息学报， 2022， 44（1）： 149-167. doi: 10.11999/JEIT210914ZHOUT， LIUY C， LUH L， et al. ResNet and its application to medical image processing： research progress and challenges［J］. Journal of Electronics & Information Technology， 2022， 44（1）： 149-167.（in Chinese）. doi: 10.11999/JEIT210914

[14] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C], 18, 7132-7141(2018).

[15] [15] 范丽丽， 赵宏伟， 赵浩宇， 等. 基于深度卷积神经网络的目标检测研究综述［J］. 光学 精密工程， 2020， 28（5）： 1152-1164.FANL L， ZHAOH W， ZHAOH Y， et al. Survey of target detection based on deep convolutional neural networks［J］. Optics and Precision Engineering， 2020， 28（5）： 1152-1164.（in Chinese）

[16] CHOWDHURY M E H, RAHMAN T, KHANDAKAR A et al. Can AI help in screening viral and COVID-19 pneumonia？[J]. IEEE Access, 8, 132665-132676(2020).

[17] RAHMAN T, KHANDAKAR A, QIBLAWEY Y et al. Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images[J]. Computers in Biology and Medicine, 132, 104319(2021).

[18] GAO S H, CHENG M M, ZHAO K et al. Res2Net： a new multi-scale backbone architecture[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 652-662(2021).

[19] HUANG G, LIU Z, VAN DER MAATEN L et al. Densely connected convolutional networks[C], 21, 2261-2269(2017).

[20] XIE S N, GIRSHICK R, DOLLAR P et al. Aggregated residual transformations for deep neural networks[C], 21, 1492-1500(2017).

[21] SANDLER M, HOWARD A, ZHU M L et al. MobileNetV2： inverted residuals and linear bottlenecks[C], 18, 4510-4520(2018).

[22] CHEN Y P, LI J N, XIAO H X et al. Dual path networks[C], 4470-4478(2017).

[23] LIU Z, LIN Y T, CAO Y et al. Swin Transformer： hierarchical Vision Transformer using Shifted Windows[C], 10, 10012-10022(2021).

[24] ALAM N, KOLAWOLE S, SETHI S et al. Vision transformers for mobile applications： a short survey[J]. arXiv preprint, 19365(2023).

[25] ZHANG H, WU C R, ZHANG Z Y et al. ResNeSt： split-attention networks[C], 19, 2736-2746(2022).

[26] LIU Z, MAO H, WU C et al. A ConvNet for the 2020s[J]. arXiv preprint(2022).