{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:51:52Z","timestamp":1776811912376,"version":"3.51.2"},"reference-count":15,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"1","license":[{"start":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T00:00:00Z","timestamp":1731542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Xinyu Science and Technology Bureau, Jiangxi Province"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational Methods in Sciences and Engineering"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>In order to improve the diagnosis rate of COVID-19 patients, according to the analysis for CT image characteristics, a method based on ResNet for CT images to identify COVID-19 patients was proposed. Combining the ResNet50 network model, by adding channel attention mechanism, adding Dropout function, and embedding the Adam optimizer of cosine annealing method, the average recognition accuracy in this method can reach to 95% for the analysis of confusion matrix results, with high accuracy and low recall rate. The results show that ResNet50 network model with Grad-CAM function has high recognition accuracy for the COVID-19 CT images. Therefore, the automatic recognition method for COVID-19 CT images has a practical application value.<\/jats:p>","DOI":"10.1177\/14727978241299518","type":"journal-article","created":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T10:26:26Z","timestamp":1745835986000},"page":"192-200","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["A recognition method of COVID-19 CT image based on ResNet network"],"prefix":"10.66113","volume":"25","author":[{"given":"Hailin","family":"Zou","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence and Data Science, Jiangxi University of Engineering"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinlan","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Data Science, Jiangxi University of Engineering"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhicheng","family":"Wen","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Data Science, Jiangxi University of Engineering"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"55691","published-online":{"date-parts":[[2024,11,14]]},"reference":[{"issue":"6","key":"e_1_3_3_2_2","first-page":"133","article-title":"An improved AlexNet model for fleece fabric quality inspection","volume":"43","author":"Jin SF","year":"2022","unstructured":"Jin SF, Hou Y, Jiao H, et al. An improved AlexNet model for fleece fabric quality inspection. Journal of Textie Research 2022; 43(6): 133\u2013139.","journal-title":"Journal of Textie Research"},{"issue":"6","key":"e_1_3_3_3_2","first-page":"1907","article-title":"A method for estimating damage degree of wheel flat scars based on time-frequency energy spectrum and VGG16","volume":"34","author":"Li DZ","year":"2022","unstructured":"Li DZ, Niu J, Liang SL, et al. A method for estimating damage degree of wheel flat scars based on time-frequency energy spectrum and VGG16. China Mech Eng 2022; 34(6); 1907\u20131914.","journal-title":"China Mech Eng"},{"issue":"7","key":"e_1_3_3_4_2","first-page":"30","article-title":"Defect detection method of apples based on GoogLeNet deep transfer learning","volume":"51","author":"Xie Y","year":"2020","unstructured":"Xie Y, Wan L, Zhang Y, et al. Defect detection method of apples based on GoogLeNet deep transfer learning. Trans Chin Soc Agric Mach 2020; 51(7): 30\u201335.","journal-title":"Trans Chin Soc Agric Mach"},{"issue":"12","key":"e_1_3_3_5_2","first-page":"197","article-title":"sGenerating image description of rice pests and diseases using a ResNet18 feature encoder","volume":"38","author":"Xie Z","year":"2022","unstructured":"Xie Z, Feng YZ, Hu YR, et al. sGenerating image description of rice pests and diseases using a ResNet18 feature encoder. Trans Chin Soc Agric Eng 2022; 38(12): 197\u2013206.","journal-title":"Trans Chin Soc Agric Eng"},{"issue":"3","key":"e_1_3_3_6_2","first-page":"452","article-title":"A fall detection algorithm based on convolutional neural network and multi-discriminant feature","volume":"35","author":"Wang X","year":"2023","unstructured":"Wang X, Zheng XY, Gao HB, et al. A fall detection algorithm based on convolutional neural network and multi-discriminant feature. J Computer-Aided Des Comput Graph 2023; 35(3): 452\u2013462.","journal-title":"J Computer-Aided Des Comput Graph"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2018.2861382"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/OJEMB.2021.3066097"},{"issue":"3","key":"e_1_3_3_9_2","first-page":"639","article-title":"A quantification method of convolutional neural network based on data standard deviation","volume":"51","author":"Huang Y","year":"2023","unstructured":"Huang Y, Zhang F, Guo W, et al. A quantification method of convolutional neural network based on data standard deviation. Acta Electron Sin 2023; 51(3): 639\u2013647.","journal-title":"Acta Electron Sin"},{"issue":"9","key":"e_1_3_3_10_2","first-page":"2265","article-title":"A review of single image super-resolution reconstruction based on deep learning","volume":"50","author":"Wu J","year":"2022","unstructured":"Wu J, Ye XJ, Huang F, et al. A review of single image super-resolution reconstruction based on deep learning. Acta Electron Sin 2022; 50(9): 2265\u20132294.","journal-title":"Acta Electron Sin"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cja.2020.12.027"},{"issue":"7","key":"e_1_3_3_12_2","first-page":"1423","article-title":"Reliability model of competing failure system with dependent degradation","volume":"41","author":"Yang ZY","year":"2020","unstructured":"Yang ZY, Zhao JM, Cheng ZH, et al. Reliability model of competing failure system with dependent degradation. Acta Armamentarii 2020; 41(7): 1423\u20131433.","journal-title":"Acta Armamentarii"},{"issue":"3","key":"e_1_3_3_13_2","first-page":"23","article-title":"Generalized Grad-CAM attacking method based on adversarial patch","volume":"42","author":"Si NW","year":"2021","unstructured":"Si NW, Zhang WL, Qu D, et al. Generalized Grad-CAM attacking method based on adversarial patch. J Commun 2021; 42(3): 23\u201335.","journal-title":"J Commun"},{"issue":"1","key":"e_1_3_3_14_2","first-page":"28","article-title":"Facial expression recognition based on improved ResNet","volume":"30","author":"Wang X","year":"2023","unstructured":"Wang X, Wang G, Cui Y, et al. Facial expression recognition based on improved ResNet. The Journal of China Universities of Posts and Telecommunications 2023; 30(1): 28\u201338.","journal-title":"The Journal of China Universities of Posts and Telecommunications"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2868382"},{"issue":"2","key":"e_1_3_3_16_2","first-page":"378","article-title":"Identification of feed raw material type based on improved ResNet18 model","volume":"54","author":"Niu ZY","year":"2023","unstructured":"Niu ZY, Yu CY, Wu ZT, et al. Identification of feed raw material type based on improved ResNet18 model. Trans Chin Soc Agric Mach 2023; 54(2): 378\u2013385.","journal-title":"Trans Chin Soc Agric Mach"}],"container-title":["Journal of Computational Methods in Sciences and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14727978241299518","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/14727978241299518","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14727978241299518","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:07:30Z","timestamp":1776809250000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/14727978241299518"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,14]]},"references-count":15,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1177\/14727978241299518"],"URL":"https:\/\/doi.org\/10.1177\/14727978241299518","relation":{},"ISSN":["1472-7978","1875-8983"],"issn-type":[{"value":"1472-7978","type":"print"},{"value":"1875-8983","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,14]]}}}