{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T02:05:19Z","timestamp":1768097119142,"version":"3.49.0"},"reference-count":133,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T00:00:00Z","timestamp":1646006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"This work was supported by the Deanship of Scientific Research at King Saud Univer-sity, Saudi Arabia through the Research Group under Grant","award":["RG-1438-071."],"award-info":[{"award-number":["RG-1438-071."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>COVID-19 has evolved into one of the most severe and acute illnesses. The number of deaths continues to climb despite the development of vaccines and new strains of the virus have appeared. The early and precise recognition of COVID-19 are key in viably treating patients and containing the pandemic on the whole. Deep learning technology has been shown to be a significant tool in diagnosing COVID-19 and in assisting radiologists to detect anomalies and numerous diseases during this epidemic. This research seeks to provide an overview of novel deep learning-based applications for medical imaging modalities, computer tomography (CT) and chest X-rays (CXR), for the detection and classification COVID-19. First, we give an overview of the taxonomy of medical imaging and present a summary of types of deep learning (DL) methods. Then, utilizing deep learning techniques, we present an overview of systems created for COVID-19 detection and classification. We also give a rundown of the most well-known databases used to train these networks. Finally, we explore the challenges of using deep learning algorithms to detect COVID-19, as well as future research prospects in this field.<\/jats:p>","DOI":"10.3390\/s22051890","type":"journal-article","created":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T20:11:57Z","timestamp":1646079117000},"page":"1890","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Study of Different Deep Learning Methods for Coronavirus (COVID-19) Pandemic: Taxonomy, Survey and Insights"],"prefix":"10.3390","volume":"22","author":[{"given":"Lamia","family":"Awassa","sequence":"first","affiliation":[{"name":"Faculty of Sciences and Technology of Sidi Bouzid, University of Kairouan, Kairouan 3100, Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7937-941X","authenticated-orcid":false,"given":"Imen","family":"Jdey","sequence":"additional","affiliation":[{"name":"Faculty of Sciences and Technology of Sidi Bouzid, University of Kairouan, Kairouan 3100, Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4668-7840","authenticated-orcid":false,"given":"Habib","family":"Dhahri","sequence":"additional","affiliation":[{"name":"Faculty of Sciences and Technology of Sidi Bouzid, University of Kairouan, Kairouan 3100, Tunisia"},{"name":"Department of Information Science, College of Applied Computer Sciences, King Saud University, Riyadh 11451, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3571-417X","authenticated-orcid":false,"given":"Ghazala","family":"Hcini","sequence":"additional","affiliation":[{"name":"Faculty of Sciences and Technology of Sidi Bouzid, University of Kairouan, Kairouan 3100, Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4163-7625","authenticated-orcid":false,"given":"Awais","family":"Mahmood","sequence":"additional","affiliation":[{"name":"Department of Information Science, College of Applied Computer Sciences, King Saud University, Riyadh 11451, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Esam","family":"Othman","sequence":"additional","affiliation":[{"name":"Department of Information Science, College of Applied Computer Sciences, King Saud University, Riyadh 11451, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad","family":"Haneef","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Foundation University Islamabad, Islamabad 44000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e9725","DOI":"10.7717\/peerj.9725","article-title":"A comparison of COVID-19, SARS and MERS","volume":"8","author":"Hu","year":"2020","journal-title":"PeerJ"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.diii.2020.03.014","article-title":"COVID-19 pneumonia: A review of typical CT findings and differential diagnosis","volume":"101","author":"Hani","year":"2020","journal-title":"Diagn. Interv. Imaging"},{"key":"ref_3","unstructured":"Zhang, Y., Niu, S., Qiu, Z., Wei, Y., Zhao, P., Yao, J., Huang, J., Wu, Q., and Tan, M. (2020). COVID-da: Deep domain adaptation from typical pneumonia to COVID-19. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"132665","DOI":"10.1109\/ACCESS.2020.3010287","article-title":"Can AI Help in Screening Viral and COVID-19 Pneumonia?","volume":"8","author":"Chowdhury","year":"2020","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.47419\/bjbabs.v2i01.25","article-title":"Herbal medicine as an alternative method to treat and prevent COVID-19","volume":"2","author":"Ahmed","year":"2021","journal-title":"Baghdad J. Biochem. Appl. Biol. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"95","DOI":"10.15585\/mmwr.mm7003e2","article-title":"Emergence of SARS-CoV-2 b. 1.1. 7 lineage\u2014united states, december 29, 2020\u2013january 12, 2021","volume":"70","author":"Galloway","year":"2021","journal-title":"Morb. Mortal. Wkly. Rep."},{"key":"ref_7","unstructured":"Madhi, S.A., Baillie, V., Cutland, C.L., Voysey, M., Koen, A.L., Fairlie, L., Padayachee, S.D., Dheda, K., Barnabas, S.L., and Bhorat, Q.E. (2021). Safety and efficacy of the ChAdOx1 nCoV-19 (AZD1222) COVID-19 vaccine against the B. 1.351 variant in South Africa. medRxiv."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Naveca, F., da Costa, C., Nascimento, V., Souza, V., Corado, A., Nascimento, F., Costa, \u00c1., Duarte, D., Silva, G., and Mej\u00eda, M. (2021, December 17). SARS-CoV-2 Reinfection by the New Variant of Concern (VOC) P. 1 in Amazonas, Brazil. Available online: Virological.org.","DOI":"10.21203\/rs.3.rs-318392\/v1"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1016\/j.cmi.2021.05.022","article-title":"Novel SARS-CoV-2 variants: The pandemics within the pandemic","volume":"27","author":"Boehm","year":"2021","journal-title":"Clin. Microbiol. Infect."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hunter, P.R., JBrainard, S., and Grant, A.R. (2021). The Impact of the November 2020 English National Lockdown on COVID-19 case counts. medRxiv.","DOI":"10.1101\/2021.01.03.21249169"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Volz, E., Mishra, S., Chand, M., Barrett, J.C., Johnson, R., Geidelberg, L., Hinsley, W.R., Laydon, D.J., Dabrera, G., and O\u2019Toole, \u00c1. (2021). Transmission of SARS-CoV-2 Lineage B. 1.1. 7 in England: Insights from linking epidemiological and genetic data. medRxiv.","DOI":"10.1038\/s41586-021-03470-x"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"van Oosterhout, C., Hall, N., Ly, H., and Tyler, K.M. (2021). COVID-19 Evolution during the Pandemic\u2013Implications of New SARS-CoV-2 Variants on Disease Control and Public Health Policies, Taylor & Francis.","DOI":"10.1080\/21505594.2021.1877066"},{"key":"ref_13","first-page":"1301","article-title":"Automatic Detection of COVID-19 Using Chest X-ray Images and Modified ResNet18-Based Convolution Neural Networks","volume":"66","author":"Katheeth","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"ref_14","unstructured":"Zhang, J., Xie, Y., Li, Y., Shen, C., and Xia, Y. (2020). COVID-19 screening on chest x-ray images using deep learning based anomaly detection. arXiv."},{"key":"ref_15","first-page":"10","article-title":"Early history of X rays","volume":"25","author":"Assmus","year":"1995","journal-title":"Beam Line"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"W105","DOI":"10.1097\/RTI.0000000000000533","article-title":"Diagnostic performance of chest X-ray for COVID-19 pneumonia during the SARS-CoV-2 pandemic in Lombardy, Italy","volume":"35","author":"Schiaffino","year":"2020","journal-title":"J. Thorac. Imaging"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Filler, A. (2009). The history, development and impact of computed imaging in neurological diagnosis and neurosurgery: CT, MRI, and DTI. Nat. Preced., 1.","DOI":"10.1038\/npre.2009.3267.4"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"154","DOI":"10.3174\/ajnr.A4967","article-title":"Performance of CT in the preoperative diagnosis of cervical lymph node metastasis in patients with papillary thyroid cancer: A systematic review and meta-analysis","volume":"38","author":"Suh","year":"2017","journal-title":"Am. J. Neuroradiol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"109151","DOI":"10.1016\/j.ejrad.2020.109151","article-title":"The performance of non-ECG gated chest CT for cardiac assessment\u2013The cardiac pathologies in chest CT (CaPaCT) study","volume":"130","author":"Eijsvoogel","year":"2020","journal-title":"Eur. J. Radiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"E81","DOI":"10.1148\/radiol.2020202568","article-title":"Efficacy of Chest CT for COVID-19 Pneumonia Diagnosis in France","volume":"298","author":"Herpe","year":"2021","journal-title":"Radiology"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"012045","DOI":"10.1088\/1742-6596\/1228\/1\/012045","article-title":"Deep learning: A branch of machine learning","volume":"1228","author":"Kumar","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2525","DOI":"10.1007\/s00259-020-04953-1","article-title":"Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software","volume":"47","author":"Zhang","year":"2020","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1038\/s41746-021-00399-3","article-title":"COVIDCTNet: An open-source deep learning approach to diagnose COVID-19 using small cohort of CT images","volume":"4","author":"Javaheri","year":"2021","journal-title":"NPJ Digit. Med."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Luz, E., Silva, P., Silva, R., Silva, L., Guimar\u00e3es, J., Miozzo, G., Moreira, G., and Menotti, D. (2021). Towards an effective and efficient deep learning model for COVID-19 patterns detection in X-ray images. Res. Biomed. Eng.","DOI":"10.1007\/s42600-021-00151-6"},{"key":"ref_26","unstructured":"Pathak, Y., Shukla, P., Tiwari, A., Stalin, S., and Singh, S. (2020). Deep Transfer Learning Based Classification Model for COVID-19 Disease. IRBM, in press."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/s11548-020-02286-w","article-title":"Automated detection of COVID-19 using ensemble of transfer learning with deep convolutional neural network based on CT scans","volume":"16","author":"Gifani","year":"2020","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5527923","DOI":"10.1155\/2021\/5527923","article-title":"Detection of COVID-19 from CT Lung Scans Using Transfer Learning","volume":"2021","author":"Lawton","year":"2021","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Duran-Lopez, L., Dominguez-Morales, J., Corral-Jaime, J., Vicente-Diaz, S., and Linares-Barranco, A. (2020). COVID-XNet: A Custom Deep Learning System to Diagnose and Locate COVID-19 in Chest X-ray Images. Appl. Sci., 10.","DOI":"10.3390\/app10165683"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"171575","DOI":"10.1109\/ACCESS.2020.3025010","article-title":"DL-CRC: Deep Learning-Based Chest Radiograph Classification for COVID-19 Detection: A Novel Approach","volume":"8","author":"Sakib","year":"2020","journal-title":"IEEE Access"},{"key":"ref_31","first-page":"3259","article-title":"Automatic Detection of COVID-19 Using a Stacked Denoising Convolutional Autoencoder","volume":"69","author":"Dhahri","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"ref_32","unstructured":"Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y. (2016). Deep Learning, MIT Press."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"32832","DOI":"10.4249\/scholarpedia.32832","article-title":"Deep Learning","volume":"10","author":"Schmidhuber","year":"2015","journal-title":"Scholarpedia"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"100412","DOI":"10.1016\/j.imu.2020.100412","article-title":"A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images","volume":"20","author":"Islam","year":"2020","journal-title":"Inform. Med. Unlocked"},{"key":"ref_35","unstructured":"Hurson, A.R., and Wu, S. (2021). Chapter Two\u2014Design of Cyber-Physical-Social Systems with Forensic-Awareness Based on Deep Learning in AI and Cloud Computing, Elsevier."},{"key":"ref_36","unstructured":"Leo, M., and Farinella, G.M. (2018). Chapter 5\u2014Computer Vision for Human\u2013Machine Interaction. Computer Vision for Assistive Healthcare, Academic Press."},{"key":"ref_37","first-page":"5425","article-title":"Hyperparameter optimization in customized convolutional neural network for blood cells classification","volume":"99","author":"Hcini","year":"2021","journal-title":"J. Theor. Appl. Inf. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"103627","DOI":"10.1016\/j.jbi.2020.103627","article-title":"A Review on Deep Learning Approaches in Healthcare Systems: Taxonomies, Challenges, and Open Issues","volume":"113","author":"Shamshirband","year":"2020","journal-title":"J. Biomed. Inform."},{"key":"ref_39","unstructured":"Hua, Y., Guo, J., and Zhao, H. (2015, January 17\u201318). Deep belief networks and deep learning. Proceedings of the 2015 International Conference on Intelligent Computing and Internet of Things, Harbin, China."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5947","DOI":"10.4249\/scholarpedia.5947","article-title":"Deep belief networks","volume":"4","author":"Hinton","year":"2009","journal-title":"Scholarpedia"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"107960","DOI":"10.1016\/j.cie.2022.107960","article-title":"Reinforcement learning based framework for COVID-19 resource allocation","volume":"167","author":"Zong","year":"2022","journal-title":"Comput. Ind. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1186\/s12880-020-00529-5","article-title":"COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet","volume":"21","author":"Saood","year":"2021","journal-title":"BMC Med. Imaging"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1109\/TNNLS.2021.3054746","article-title":"Anam-Net: Anamorphic Depth Embedding-Based Lightweight CNN for Segmentation of Anomalies in COVID-19 Chest CT Images","volume":"32","author":"Paluru","year":"2021","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"130","DOI":"10.3390\/electronics11010130","article-title":"SD-UNet: A Novel Segmentation Framework for CT Images of Lung Infections","volume":"11","author":"SYin","year":"2022","journal-title":"Electronics"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1002\/mp.14609","article-title":"Abnormal lung quantification in chest CT images of COVID-19 patients with deep learning and its application to severity prediction","volume":"48","author":"Shan","year":"2020","journal-title":"Med. Phys."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12967-021-02992-2","article-title":"Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19","volume":"19","author":"Fung","year":"2021","journal-title":"J. Transl. Med."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Suri, J., Agarwal, S., Pathak, R., Ketireddy, V., Columbu, M., Saba, L., Gupta, S., Faa, G., Singh, I., and Turk, M. (2021). COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models. Diagnostics, 11.","DOI":"10.3390\/diagnostics11081405"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"108452","DOI":"10.1016\/j.patcog.2021.108452","article-title":"Deep co-supervision and attention fusion strategy for automatic COVID-19 lung infection segmentation on CT images","volume":"124","author":"Hu","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_49","first-page":"844","article-title":"A deep learning framework to detect COVID-19 disease via chest X-ray and CT scan images","volume":"11","author":"Kamil","year":"2021","journal-title":"Int. J. Electr. Comput. Eng. IJECE"},{"key":"ref_50","first-page":"365","article-title":"Recognition of corona virus disease (COVID-19) using deep learning network","volume":"11","author":"Abdulmunem","year":"2021","journal-title":"Int. J. Electr. Comput. Eng. IJECE"},{"key":"ref_51","unstructured":"Gozes, O., Frid-Adar, M., Sagie, N., Zhang, H., Ji, W., and Greenspan, H. (2020). Coronavirus detection and analysis on chest ct with deep learning. arXiv."},{"key":"ref_52","first-page":"2","article-title":"Labeled optical coherence tomography (OCT) and Chest X-ray images for classification","volume":"2","author":"Kermany","year":"2018","journal-title":"Mendeley Data"},{"key":"ref_53","unstructured":"Cohen, J.P., Morrison, P., and Dao, L. (2020). COVID-19 Image Data Collection. arXiv."},{"key":"ref_54","unstructured":"(2020, June 03). Italian Society of Medical and Interventional Radiology (SIRM). Available online: https:\/\/www.sirm.org\/en\/category\/articles\/covid-19-database\/page\/1\/."},{"key":"ref_55","unstructured":"Manapure, P., Likhar, K., and Kosare, H. (2021, December 17). Detecting COVID-19 in X-ray Images with Keras, Tensor Flow, and Deep Learning. Available online: http:\/\/acors.org\/Journal\/Papers\/Volume1\/issue3\/VOL1_ISSUE3_09.pdf."},{"key":"ref_56","unstructured":"(2021, December 17). ChainZ. Available online: www.ChainZ.cn."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00138-020-01119-9","article-title":"A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis","volume":"32","author":"Zhang","year":"2020","journal-title":"Mach. Vis. Appl."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Ahrabi, S.S., Scarpiniti, M., Baccarelli, E., and Momenzadeh, A. (2021). An Accuracy vs. Complexity Comparison of Deep Learning Architectures for the Detection of COVID-19 Disease. Computation, 9.","DOI":"10.3390\/computation9010003"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s12539-020-00408-1","article-title":"Classification of COVID-19 by Compressed Chest CT Image through Deep Learning on a Large Patients Cohort","volume":"13","author":"Zhu","year":"2021","journal-title":"Interdiscip. Sci. Comput. Life Sci."},{"key":"ref_60","first-page":"1719","article-title":"Intelligent Decision Support System for COVID-19 Empowered with Deep Learning","volume":"66","author":"Siddiqui","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Hajij, M., Zamzmi, G., and Batayneh, F. (2021). TDA-Net: Fusion of Persistent Homology and Deep Learning Features for COVID-19 Detection in Chest X-ray Images. arXiv.","DOI":"10.1109\/EMBC46164.2021.9629828"},{"key":"ref_62","unstructured":"NIH (2021, December 17). Nih Chest X-ray Dataset of 14 Common Thorax Disease, Available online: https:\/\/www.nih.gov\/news-events\/news-releases\/nih-clinical-center-provides-one-largestpublicly-available-chest-x-ray-datasets-scientific-community."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"6096","DOI":"10.1007\/s00330-021-07715-1","article-title":"A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)","volume":"31","author":"Wang","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"102588","DOI":"10.1016\/j.bspc.2021.102588","article-title":"A fully automated deep learning-based network for detecting COVID-19 from a new and large lung CT scan dataset","volume":"68","author":"Rahimzadeh","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Dhiman, G., Chang, V., Singh, K.K., and Shankar, A. (2021). ADOPT: Automatic deep learning and optimization-based approach for detection of novel coronavirus COVID-19 disease using X-ray images. J. Biomol. Struct. Dyn., 1\u201313.","DOI":"10.1080\/07391102.2021.1875049"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"102411","DOI":"10.1016\/j.ipm.2020.102411","article-title":"CGNet: A graph-knowledge embedded convolutional neural network for detection of pneumonia","volume":"58","author":"Yu","year":"2020","journal-title":"Inf. Process. Manag."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"E167","DOI":"10.1148\/radiol.2020203511","article-title":"DeepCOVID-XR: An Artificial Intelligence Algorithm to Detect COVID-19 on Chest Radiographs Trained and Tested on a Large US Clinical Data Set","volume":"299","author":"Wehbe","year":"2021","journal-title":"Radiology"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Sedik, A., Hammad, M., El-Samie, F.E.A., Gupta, B.B., and El-Latif, A.A.A. (2021). Efficient deep learning approach for augmented detection of Coronavirus disease. Neural Comput. Appl., 1\u201318.","DOI":"10.1007\/s00521-020-05410-8"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"100505","DOI":"10.1016\/j.imu.2020.100505","article-title":"EMCNet: Automated COVID-19 diagnosis from X-ray images using convolutional neural network and ensemble of machine learning classifiers","volume":"22","author":"Saha","year":"2020","journal-title":"Inform. Med. Unlocked"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1016\/j.bbe.2020.08.008","article-title":"A deep learning approach to detect COVID-19 coronavirus with X-ray images","volume":"40","author":"Jain","year":"2020","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_71","unstructured":"Alom, M.Z., Rahman, M.M., Nasrin, M.S., Taha, T.M., and Asari, V.K. (2020). COVID_MTNet: COVID-19 detection with multi-task deep learning approaches. arXiv."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Zheng, C., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., and Wang, X. (2020). Deep learning-based detection for COVID-19 from chest CT using weak label. MedRxiv.","DOI":"10.1101\/2020.03.12.20027185"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1007\/s10140-020-01886-y","article-title":"Diagnosis of COVID-19 using CT scan images and deep learning techniques","volume":"28","author":"Shah","year":"2021","journal-title":"Emerg. Radiol."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","article-title":"The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository","volume":"26","author":"Clark","year":"2013","journal-title":"J. Digit. Imaging"},{"key":"ref_75","unstructured":"Desai, S., Baghal, A., Wongsurawat, T., Al-Shukri, S., Gates, K., Farmer, P., Rutherford, M., Blake, G.D., Nolan, T., and Powell, T. (2020). Data from chest imaging with clinical and genomic correlates representing a rural COVID-19 positive population. Cancer Imaging Arch."},{"key":"ref_76","unstructured":"Zhao, J., Zhang, Y., He, X., and Xie, P. (2020). COVID-ct-dataset: A ct scan dataset about COVID-19. arXiv."},{"key":"ref_77","unstructured":"(2021, December 17). COVID-CTset. Available online: https:\/\/github.com\/mr7495\/COVID-CTset."},{"key":"ref_78","unstructured":"(2021, December 17). Chest X-ray Images (Pneumonia). Available online: https:\/\/www.kaggle.com\/paultimothymooney\/chest-xray-pneumonia\/version\/1."},{"key":"ref_79","unstructured":"(2020, April 09). UCSD-AI4H. COVID-CT. Available online: https:\/\/github.com\/UCSD-AI4H\/COVID-CT."},{"key":"ref_80","unstructured":"Alqudah, A.M., and Qazan, S. (2021, December 17). Augmented COVID-19 X-ray; Volume 4. Available online: https:\/\/data.mendeley.com\/datasets\/2fxz4px6d8\/4."},{"key":"ref_81","unstructured":"(2021, December 17). COVID-19 Radiography Database. Available online: https:\/\/www.kaggle.com\/tawsifurrahman\/covid19-radiography-database."},{"key":"ref_82","unstructured":"(2020, June 03). COVID-19 Radiopaedia. Available online: https:\/\/radiopaedia.org\/articles\/covid-19-3?lang=us."},{"key":"ref_83","first-page":"1436","article-title":"Automatic Classification of the Severity of COVID-19 Patients Based on CT Scans and X-rays Using Deep Learning","volume":"7","author":"Bhatti","year":"2021","journal-title":"Eur. J. Mol. Clin. Med."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Afifi, A., Hafsa, N.E., Ali, M.A.S., Alhumam, A., and Alsalman, S. (2021). An Ensemble of Global and Local-Attention Based Convolutional Neural Networks for COVID-19 Diagnosis on Chest X-ray Images. Symmetry, 13.","DOI":"10.3390\/sym13010113"},{"key":"ref_85","unstructured":"Sarker, L., Islam, M.M., Hannan, T., and Ahmed, Z. (2021, December 17). COVID-Densenet: A Deep Learning Architecture to Detect COVID-19 from Chest Radiology Images. Available online: https:\/\/pdfs.semanticscholar.org\/c6f7\/a57a37e87b52ac92402987c9b7a3df41f2db.pdf."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1007\/s40747-020-00199-4","article-title":"Cascaded deep learning classifiers for computer-aided diagnosis of COVID-19 and pneumonia diseases in X-ray scans","volume":"7","author":"Karar","year":"2020","journal-title":"Complex Intell. Syst."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1007\/s10489-020-01867-1","article-title":"COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization","volume":"51","author":"Zebin","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Ibrahim, A.U., Ozsoz, M., Serte, S., Al-Turjman, F., and Yakoi, P.S. (2021). Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19. Cogn. Comput., 1\u201313.","DOI":"10.1007\/s12559-020-09787-5"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1690","DOI":"10.1007\/s10489-020-01902-1","article-title":"Deep learning based detection and analysis of COVID-19 on chest X-ray images","volume":"51","author":"Jain","year":"2020","journal-title":"Appl. Intell."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Wang, N., Liu, H., and Xu, C. (2020, January 17\u201319). Deep learning for the detection of COVID-19 using transfer learning and model integration. Proceedings of the 2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC), Beijing, China.","DOI":"10.1109\/ICEIEC49280.2020.9152329"},{"key":"ref_91","first-page":"3615","article-title":"Using X-ray images and deep learning for automated detection of coronavirus disease","volume":"39","author":"Chawki","year":"2020","journal-title":"J. Biomol. Struct. Dyn."},{"key":"ref_92","unstructured":"Sajid, N. (2020, May 04). COVID-19 Patients Lungs X-ray Images 10000. Available online: https:\/\/www.kaggle.com\/nabeelsajid917\/covid-19-x-ray-10000-images."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"ABustos, A., Pertusa, A., Salinas, J.-M., and de la Iglesia-Vay\u00e1, M. (2019). PadChest: A large chest x-ray image dataset with multi-label annotated reports. arXiv.","DOI":"10.1016\/j.media.2020.101797"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1038\/s42256-021-00338-7","article-title":"AI for radiographic COVID-19 detection selects shortcuts over signal","volume":"3","author":"DeGrave","year":"2021","journal-title":"Nat. Mach. Intell."},{"key":"ref_95","first-page":"1","article-title":"COVID-net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images","volume":"10","author":"Wang","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_96","unstructured":"(2021, December 17). Novel Corona Virus 2019 Dataset. Available online: https:\/\/www.kaggle.com\/sudalairajkumar\/novel-corona-virus-2019-dataset."},{"key":"ref_97","unstructured":"Patel, P. (2021, December 17). Chest X-ray (COVID-19 & Pneumonia). Available online: https:\/\/www.kaggle.com\/prashant268\/chest-xray-covid19-pneumonia."},{"key":"ref_98","unstructured":"(2021, December 17). RSNA Pneumonia Detection Challenge. Available online: https:\/\/www.kaggle.com\/c\/rsna-pneumonia-detection-challenge\/data."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"106885","DOI":"10.1016\/j.asoc.2020.106885","article-title":"The ensemble deep learning model for novel COVID-19 on CT images","volume":"98","author":"Zhou","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1097\/RLI.0000000000000748","article-title":"A Deep-Learning Diagnostic Support System for the Detection of COVID-19 Using Chest Radiographs: A Multireader Validation Study","volume":"56","author":"Fontanellaz","year":"2021","journal-title":"Investig. Radiol."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"107613","DOI":"10.1016\/j.patcog.2020.107613","article-title":"Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest X-rays","volume":"110","author":"Wang","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3431804","article-title":"A Deep Learning Approach for COVID-19 8 Viral Pneumonia Screening with X-ray Images","volume":"2","author":"Ahmed","year":"2021","journal-title":"Digit. Gov. Res. Pr."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"106912","DOI":"10.1016\/j.asoc.2020.106912","article-title":"CNN-based transfer learning\u2013BiLSTM network: A novel approach for COVID-19 infection detection","volume":"98","author":"Aslan","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"106859","DOI":"10.1016\/j.asoc.2020.106859","article-title":"InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray","volume":"99","author":"Gupta","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"106744","DOI":"10.1016\/j.asoc.2020.106744","article-title":"Learning distinctive filters for COVID-19 detection from chest X-ray using shuffled residual CNN","volume":"99","author":"Karthik","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"104181","DOI":"10.1016\/j.compbiomed.2020.104181","article-title":"Lightweight deep learning models for detecting COVID-19 from chest X-ray images","volume":"130","author":"Karakanis","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"102257","DOI":"10.1016\/j.bspc.2020.102257","article-title":"MH-COVIDNet: Diagnosis of COVID-19 using deep neural networks and meta-heuristic-based feature selection on X-ray images","volume":"64","author":"Canayaz","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"110495","DOI":"10.1016\/j.chaos.2020.110495","article-title":"CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images","volume":"142","author":"EHussain","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"012007","DOI":"10.1088\/1757-899X\/1051\/1\/012007","article-title":"A Novel Aided Diagnosis Schema for COVID 19 Using Convolution Neural Network","volume":"1051","author":"Mahdi","year":"2021","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Ahmed, S., Hossain, F., and Noor, M.B.T. (2020). Convid-Net: An Enhanced Convolutional Neural Network Framework for COVID-19 Detection from X-ray Images, Springer.","DOI":"10.1007\/978-981-33-4673-4_55"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"3026","DOI":"10.1007\/s10489-020-01978-9","article-title":"Corona-Nidaan: Lightweight deep convolutional neural network for chest X-ray based COVID-19 infection detection","volume":"51","author":"Chakraborty","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"107160","DOI":"10.1016\/j.asoc.2021.107160","article-title":"DeepCoroNet: A deep LSTM approach for automated detection of COVID-19 cases from chest X-ray images","volume":"103","author":"Demir","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s42003-020-01535-7","article-title":"Fast automated detection of COVID-19 from medical images using convolutional neural networks","volume":"4","author":"Liang","year":"2021","journal-title":"Commun. Biol."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.neucom.2021.03.034","article-title":"MANet: A two-stage deep learning method for classification of COVID-19 from Chest X-ray images","volume":"443","author":"Xu","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"103805","DOI":"10.1016\/j.compbiomed.2020.103805","article-title":"COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches","volume":"121","author":"Ergen","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1109\/TMI.2020.3040950","article-title":"Viral Pneumonia Screening on Chest X-Rays Using Confidence-Aware Anomaly Detection","volume":"40","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"110245","DOI":"10.1016\/j.chaos.2020.110245","article-title":"CVDNet: A novel deep learning architecture for detection of coronavirus (COVID-19) from chest X-ray images","volume":"140","author":"Ouchicha","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_118","unstructured":"(2020, July 12). Actualmed COVID-19 Chest X-ray Dataset. Available online: https:\/\/github.com\/agchung\/Actualmed-COVID-chestxray-dataset."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.inffus.2021.04.008","article-title":"A critic evaluation of methods for COVID-19 automatic detection from X-ray images","volume":"76","author":"Maguolo","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_120","unstructured":"Chung, A. (2020, June 03). COVID Chest X-ray Dataset. Available online: https:\/\/github.com\/agchung\/Figure1-COVID-chestxray-dataset."},{"key":"ref_121","unstructured":"(2021, December 17). SARS-CoV-2 CT-Scan Datase. June 2020. Available online: https:\/\/www.kaggle.com\/plameneduardo\/sarscov2-ctscan-dataset."},{"key":"ref_122","unstructured":"(2021, December 17). COVID-19 X-ray Dataset (Train & Test Sets) with COVID-19CNN. April 2020. Available online: https:\/\/www.kaggle.com\/khoongweihao\/covid19-xray-dataset-train-test-sets."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"044506","DOI":"10.1117\/1.JMI.3.4.044506","article-title":"LUNGx Challenge for computerized lung nodule classification","volume":"3","author":"Armato","year":"2016","journal-title":"J. Med. Imaging"},{"key":"ref_124","unstructured":"(2021, December 17). COVID-19 Detection X-ray Dataset. Available online: https:\/\/kaggle.com\/darshan1504\/covid19-detection-xray-dataset."},{"key":"ref_125","unstructured":"Vay\u00e1, M.d.l.I., Saborit, J.M., Montell, J.A., Pertusa, A., Bustos, A., Cazorla, M., Galant, J., Barber, X., Orozco-Beltr\u00e1n, D., and Garcia, F. (2020). BIMCV COVID-19+: A large annotated dataset of RX and CT images from COVID-19 patients. arXiv."},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., and Summers, R.M. (2021, December 17). Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Available online: https:\/\/openaccess.thecvf.com\/content_cvpr_2017\/html\/Wang_ChestX-ray8_Hospital-Scale_Chest_CVPR_2017_paper.html.","DOI":"10.1109\/CVPR.2017.369"},{"key":"ref_127","unstructured":"(2021, December 17). COVID-19 X-ray Images. Available online: https:\/\/www.kaggle.com\/bachrr\/covid-chest-xray."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.media.2017.06.015","article-title":"Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge","volume":"42","author":"Setio","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_129","first-page":"475","article-title":"Two public chest X-ray datasets for computer-aided screening of pulmonary diseases","volume":"4","author":"Jaeger","year":"2014","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Cohen, J.P., Morrison, P., Dao, L., Roth, K., Duong, T.Q., and Ghassemi, M. (2020). COVID-19 image data collection: Prospective predictions are the future. arXiv.","DOI":"10.59275\/j.melba.2020-48g7"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"30551","DOI":"10.1109\/ACCESS.2021.3058537","article-title":"A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)","volume":"9","author":"Islam","year":"2021","journal-title":"IEEE Access"},{"key":"ref_132","unstructured":"Hemanjali, A., Revathy, S., Anu, V.M., MaryGladence, L., Jeyanthi, P., and Ritika, C.G. (2021, January 8\u201310). Document Clustering on COVID literature using Machine Learning. Proceedings of the 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/5\/1890\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:29:16Z","timestamp":1760135356000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/5\/1890"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,28]]},"references-count":133,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22051890"],"URL":"https:\/\/doi.org\/10.3390\/s22051890","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,28]]}}}