Login Register Cart 0
Generic placeholder image

Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Review Article

COVID-19 Imaging-based AI Research - A Literature Review

Author(s): Cheng Ge, Lili Zhang, Liangxu Xie, Ren Kong, Hong Zhang and Shan Chang*

Volume 18, Issue 5, 2022

Published on: 10 January, 2022

Article ID: e020921196055 Pages: 13

DOI: 10.2174/1573405617666210902103729

Price: $65

conference banner
Abstract

Background: The new coronavirus disease 2019 (COVID-19) is spreading rapidly around the world. Artificial Intelligence (AI) assisted identification and detection of diseases is an effective method of medical diagnosis.

Objectives: To present recent advances in AI-assisted diagnosis of COVID-19, we introduce major aspects of AI in the process of diagnosing COVID-19.

Methods: In this paper, we firstly cover the latest collection and processing methods of datasets of COVID-19. The processing methods mainly include building public datasets, transfer learning, unsupervised learning and weakly supervised learning, semi-supervised learning methods and so on. Secondly, we introduce the algorithm application and evaluation metrics of AI in medical imaging segmentation and automatic screening. Then, we introduce the quantification and severity assessment of infection in COVID-19 patients based on image segmentation and automatic screening. Finally, we analyze and point out the current AI-assisted diagnosis of COVID-19 problems, which may provide useful clues for future work.

Conclusion: AI is critical for COVID-19 diagnosis. Combining chest imaging with AI can not only save time and effort, but also provide more accurate and efficient medical diagnosis results.

Keywords: COVID-19, artificial intelligence, medical diagnosis, medical imaging, segmentation, AI-assisted.

Graphical Abstract

[1]
WHO. laboratory testing for 2019 novel coro-navirus (2019-ncov) in suspected human cases. 2020. Available from: https://www.who.int/publications/i/item/10665-33150121 Aug. 2020
[2]
Ye Z, Zhang Y, Wang Y, Huang Z, Song B. Chest CT manifestations of new coronavirus disease 2019 (COVID-19): A pictorial review. Eur Radiol 2020; 30(8): 4381-9.
[http://dx.doi.org/10.1007/s00330-020-06801-0] [PMID: 32193638]
[3]
Fang Y, Zhang H, Xie J, et al. Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR. Radiology 2020; 296(2): E115-7.
[http://dx.doi.org/10.1148/radiol.2020200432] [PMID: 32073353]
[4]
Ai T, Yang Z, Hou H, et al. Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: A report of 1014 cases. Radiology 2020; 296(2): E32-40.
[http://dx.doi.org/10.1148/radiol.2020200642] [PMID: 32101510]
[5]
Cohen JP, Morrison P, Dao L, Roth K, Duong TQ, Ghassemi M. Covid-19 image data collection: Prospective predictions are the future. preprint arXiv:200611988 2020.
[6]
Zhao J, Zhang Y, He X, Xie P. COVID-CT-Dataset: A CT scan dataset about COVID-19. arXiv preprint arXiv:200313865 2020.
[7]
Ma J, Wang Y, An X, et al. Towards efficient COVID-19 CT annotation: A Benchmark for lung and infection segmentation. preprint arXiv:200412537 2020.
[8]
Born J, Brändle G, Cossio M, et al. POCOVID-Net: Automatic detection of COVID-19 from a new lung ultrasound imaging dataset (POCUS). arXiv preprint arXiv:200412084 2020.
[9]
Morozov S, Andreychenko A, Pavlov N, et al. MosMedData: Chest CT scans with COVID-19 related findings dataset. arXiv preprint arXiv:200506465 2020.
[10]
Peng Y, Tang Y-X, Lee S, Zhu Y, Summers RM, Lu Z. COVID-19-CT-CXR: A freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature. arXiv preprint arXiv:200606177 2020.
[11]
Wang L, Lin ZQ, Wong A. COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci Rep 2020; 10(1): 19549.
[http://dx.doi.org/10.1038/s41598-020-76550-z] [PMID: 33177550]
[12]
Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L, Eds. Imagenet: A large-scale hierarchical image database.2009 IEEE conference on computer vision and pattern recognition. Ieee 2009.
[13]
He X, Yang X, Zhang S, et al. Sample-efficient deep learning for COVID-19 diagnosis based on CT scans. medRxi 2020.
[14]
Bassi PR, Attux R. A Deep Convolutional Neural Network for COVID-19 Detection Using Chest X-Rays. arXiv preprint arXiv:200501578 2020.
[15]
Shi F, Xia L, Shan F, et al. Large-scale screening of covid-19 from community acquired pneumonia using infection size-aware classification. arXiv preprint arXiv:200309860 2020.
[16]
Zhou J, Jing B, Wang Z. SODA: Detecting Covid-19 in Chest X-rays with Semi-supervised Open Set Domain Adaptation. arXiv preprint arXiv:200511003 2020.
[17]
Abbas A, Abdelsamea MM, Gaber MM. Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. arXiv preprint arXiv:200313815 2020.
[http://dx.doi.org/10.1101/2020.03.30.20047456]
[18]
Hall LO, Paul R, Goldgof DB, Goldgof GM. Finding covid-19 from chest x-rays using deep learning on a small dataset. arXiv preprint arXiv:200402060 2020.
[19]
Makris A, Kontopoulos I, Tserpes K, Eds. COVID-19 detection from chest X-Ray images using Deep Learning and Convolutional Neural Networks. 11th Hellenic Conference on Artificial Intelligence.
[http://dx.doi.org/10.1145/3411408.3411416]
[20]
Narin A, Kaya C, Pamuk Z. Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. arXiv preprint arXiv:200310849 2020.
[21]
Nishio M, Noguchi S, Matsuo H, Murakami T. Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: Combination of data augmentation methods in a small dataset. arXiv preprint arXiv:200600730 2020.
[22]
Pathari S, Rahul U. Automatic Detection of COVID-19 and Pneumonia from Chest X-Ray using Transfer Learning. medRxiv 2020.
[23]
Tahir A, Qiblawey Y, Khandakar A, et al. Coronavirus: Comparing COVID-19, SARS and MERS in the eyes of AI. arXiv preprint arXiv:200511524 2020.
[24]
Wang S, Kang B, Ma J, et al. A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). MedRxiv 2020.
[http://dx.doi.org/10.1101/2020.02.14.20023028]
[25]
Afshar P, Heidarian S, Naderkhani F, Oikonomou A, Plataniotis KN, Mohammadi A. Covid-caps: A capsule network-based framework for identification of covid-19 cases from X-ray images. arXiv preprint arXiv:200402696 2020.
[26]
Alom MZ, Rahman M, Nasrin MS, Taha TM, Asari VK. COVID_MTNet: COVID-19 detection with multi-task deep learning approaches. arXiv preprint arXiv:200403747 2020.
[27]
Apostolopoulos ID, Mpesiana TA. Covid-19: Automatic detection from X-ray images utilizing transfer learning with convolutional neural networks.Physical and Engineering Sciences in Medicine. 2020; 1.
[28]
Basu S, Mitra S, Saha N, Eds. Deep learning for screening COVID-19 using chest x-ray images. arxiv: 2004; 10507.
[http://dx.doi.org/10.1109/SSCI47803.2020.9308571]
[29]
Chowdhury ME, Rahman T, Khandakar A, et al. Can AI help in screening viral and COVID-19 pneumonia? arXiv preprint arXiv:200313145 2020.
[30]
Khalifa NEM, Taha MHN, Hassanien AE, Elghamrawy S. Detection of coronavirus (COVID-19) associated pneumonia based on generative adversarial networks and a fine-tuned deep transfer learning model using chest X-ray dataset. arXiv preprint arXiv:200401184 2020.
[31]
Misra S, Jeon S, Lee S, Managuli R, Jang I-S, Kim C. Multi-channel transfer learning of chest x-ray images for screening of covid-19. Electronics (Basel) 2020; 9(9): 1388.
[http://dx.doi.org/10.3390/electronics9091388]
[32]
Wang X, Deng X, Fu Q, et al. A weakly-supervised framework for COVID-19 classification and lesion localization from chest CT. IEEE Trans Med Imaging 2020; 39(8): 2615-25.
[http://dx.doi.org/10.1109/TMI.2020.2995965] [PMID: 33156775]
[33]
Laradji I, Rodriguez P, Manas O, Eds. A weakly supervised consistency-based learning method for COVID-19 segmentation in CT images. arxiv 2020.
[34]
Hu S, Gao Y, Niu Z, et al. Weakly supervised deep learning for covid-19 infection detection and classification from ct images. IEEE Access 2020; 8: 118869-83.
[http://dx.doi.org/10.1109/ACCESS.2020.3005510]
[35]
Fan D-P, Zhou T, Ji G-P, et al. Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images. IEEE Trans Med Imaging 2020; 39(8): 2626-37.
[http://dx.doi.org/10.1109/TMI.2020.2996645] [PMID: 32730213]
[36]
Selvan R, Dam EB, Rischel S, Sheng K, Nielsen M, Pai A. Lung segmentation from chest X-rays using variational data imputation. arXiv preprint arXiv:200510052 2020.
[37]
Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. Adv Neural Inf Process Syst 2014; 27: 2672-80.
[38]
Loey M, Smarandache F. M Khalifa NE. Within the lack of chest COVID-19 X-ray dataset: A novel detection model based on GAN and deep transfer learning. Symmetry (Basel) 2020; 12(4): 651.
[http://dx.doi.org/10.3390/sym12040651]
[39]
Albahli S. Efficient GAN-based chest radiographs (CXR) augmentation to diagnose coronavirus disease pneumonia. Int J Med Sci 2020; 17(10): 1439-48.
[http://dx.doi.org/10.7150/ijms.46684] [PMID: 32624700]
[40]
Shan F, Gao Y, Wang J, et al. Lung infection quantification of covid-19 in ct images with deep learning. arXiv preprint arXiv:200304655 2020.
[41]
Jin S, Wang B, Xu H, et al. AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system in four weeks. medRxiv 2020.
[http://dx.doi.org/10.1101/2020.03.19.20039354]
[42]
Li L, Qin L, Xu Z et al. Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology 2020; 200905.
[43]
Wang J, Bao Y, Wen Y, et al. Prior-attention residual learning for more discriminative COVID-19 screening in CT images. IEEE Trans Med Imaging 2020; 39(8): 2572-83.
[http://dx.doi.org/10.1109/TMI.2020.2994908] [PMID: 32730210]
[44]
Xie W, Jacobs C, Charbonnier J-P, van Ginneken B. Relational modeling for robust and efficient pulmonary lobe segmentation in CT scans. IEEE Trans Med Imaging 2020; 39(8): 2664-75.
[http://dx.doi.org/10.1109/TMI.2020.2995108] [PMID: 32730216]
[45]
Zhang K, Liu X, Shen J, et al. Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography. Cell 2020; 181(6): 1423-33. e11.
[http://dx.doi.org/10.1016/j.cell.2020.08.029]
[46]
Voulodimos A, Protopapadakis E, Katsamenis I, Doulamis A, Doulamis N. Deep learning models for COVID-19 infected area segmentation in CT images. medRxiv 2020.
[47]
Qiu Y, Liu Y, Xu J. MiniSeg: An extremely minimum network for efficient COVID-19 segmentation. arXiv preprint arXiv:200409750 2020.
[48]
Liu S, Georgescu B, Xu Z, et al. 3D tomographic pattern synthesis for enhancing the quantification of COVID-19. arXiv preprint arXiv:200501903 2020.
[49]
Rajinikanth V, Kadry S, Thanaraj KP, Kamalanand K, Seo S. Firefly-algorithm supported scheme to detect COVID-19 lesion in lung CT scan images using shannon entropy and markov-random-field. arXiv preprint arXiv:200409239 2020.
[50]
Yan Q, Wang B, Gong D, et al. COVID-19 chest CT image segmentation--A deep convolutional neural network solution. arXiv preprint arXiv:200410987 2020.
[51]
Wang G, Liu X, Li C, et al. A noise-robust framework for automatic segmentation of COVID-19 pneumonia lesions from CT images. IEEE Trans Med Imaging 2020; 39(8): 2653-63.
[http://dx.doi.org/10.1109/TMI.2020.3000314] [PMID: 32730215]
[52]
Chen X, Yao L, Zhang Y. Residual attention u-net for automated multi-class segmentation of COVID-19 chest CT images. arXiv preprint arXiv:200405645 2020.
[53]
Zhou T, Canu S, Ruan S. An automatic covid-19 ct segmentation network using spatial and channel attention mechanism. arXiv preprint arXiv:200406673 2020.
[54]
Wu Y-H, Gao S-H, Mei J, et al. JCS: An explainable COVID-19 diagnosis system by joint classification and segmentation. arXiv preprint arXiv:200407054 2020.
[55]
Wu W, Shi Y, Li X, et al. Deep learning to estimate the physical proportion of infected region of lung for COVID-19 pneumonia with CT image set. arXiv preprint arXiv:200605018 2020.
[56]
He K, Zhao W, Xie X, et al. Synergistic learning of lung lobe segmentation and hierarchical multi-instance classification for automated severity assessment of COVID-19 in CT images. arXiv preprint arXiv:200503832 2020.
[57]
Yeh C-F, Cheng H-T, Wei A, et al. A cascaded learning strategy for robust COVID-19 pneumonia chest x-ray screening. arXiv preprint arXiv:200412786 2020.
[58]
Signoroni A, Savardi M, Benini S, et al. End-to-end learning for semiquantitative rating of COVID-19 severity on chest X-rays. arXiv preprint arXiv:200604603 2020.
[59]
Gozes O, Frid-Adar M, Greenspan H, et al. Rapid ai development cycle for the coronavirus (covid-19) pandemic: Initial results for automated detection & patient monitoring using deep learning ct image analysis. arXiv preprint arXiv:200305037 2020.
[60]
Hu R, Ruan G, Xiang S, Huang M, Liang Q, Li J. Automated diagnosis of COVID-19 using deep learning and data augmentation on chest CT. medRxiv 2020.
[61]
Song Y, Zheng S, Li L, et al. Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. medRxiv 2020.
[62]
Jamil M, Hussain I. Automatic detection of COVID-19 infection from chest X-ray using deep learning. medRxiv 2020.
[63]
Ouyang X, Huo J, Xia L, et al. Dual-sampling attention network for diagnosis of COVID-19 from community acquired pneumonia. IEEE Trans Med Imaging 2020; 39(8): 2595-605.
[http://dx.doi.org/10.1109/TMI.2020.2995508] [PMID: 32730212]
[64]
Khuzani AZ, Heidari M, Shariati SA. COVID-Classifier: An automated machine learning model to assist in the diagnosis of COVID-19 infection in chest x-ray images. medRxiv 2020.
[65]
Ghoshal B, Tucker A. Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection. arXiv preprint arXiv:200310769 2020.
[66]
Oh Y, Park S, Ye JC. Deep learning covid-19 features on cxr using limited training data sets. IEEE Trans Med Imaging 2020; 39(8): 2688-700.
[http://dx.doi.org/10.1109/TMI.2020.2993291] [PMID: 32396075]
[67]
Zhang J, Xie Y, Li Y, Shen C, Xia Y. Covid-19 screening on chest x-ray images using deep learning based anomaly detection. arXiv preprint arXiv:200312338 2020.
[68]
Rahimzadeh M, Attar A. A new modified deep convolutional neural network for detecting COVID-19 from X-ray images. arXiv preprint arXiv:200408052 2020.
[69]
De Moura J, García LR, Vidal PFL, et al. Deep convolutional approaches for the analysis of covid-19 using chest x-ray images from portable devices. IEEE Access 2020; 8: 195594-607.
[http://dx.doi.org/10.1109/ACCESS.2020.3033762]
[70]
Farooq M, Hafeez A. Covid-resnet: A deep learning framework for screening of covid19 from radiographs. arXiv preprint arXiv:200314395 2020.
[71]
Li X, Li C, Zhu D. COVID-MobileXpert: On-device COVID-19 screening using snapshots of chest X-ray. arXiv preprint arXiv:200403042 2020.
[72]
Asif S, Wenhui Y. Automatic detection of COVID-19 using X-ray images with deep convolutional neural networks and machine learning. medRxiv 2020.
[73]
González G, Bustos A, Salinas JM, et al. UMLS-ChestNet: A deep convolutional neural network for radiological findings, differential diagnoses and localizations of COVID-19 in chest x-rays. arXiv preprint arXiv:200605274 2020.
[74]
Lv D, Qi W, Li Y, Sun L, Wang Y. A cascade network for detecting COVID-19 using chest X-rays. arXiv preprint arXiv:200501468 2020.
[75]
Jin C, Chen W, Cao Y, et al. Development and evaluation of an AI system for COVID-19 diagnosis. medRxiv 2020.
[http://dx.doi.org/10.1101/2020.03.20.20039834]
[76]
Karim M, Döhmen T, Rebholz-Schuhmann D, Decker S, Cochez M, Beyan O. Deepcovidexplainer: Explainable covid-19 predictions based on chest X-ray images. arXiv preprint arXiv:200404582 2020.
[77]
Castiglioni I, Ippolito D, Interlenghi M, et al. Artificial intelligence applied on chest X-ray can aid in the diagnosis of COVID-19 infection: A first experience from Lombardy, Italy. medRxiv 2020.
[http://dx.doi.org/10.1101/2020.04.08.20040907]
[78]
Minaee S, Kafieh R, Sonka M, Yazdani S, Soufi GJ. Deep-covid: Predicting covid-19 from chest x-ray images using deep transfer learning. arXiv preprint arXiv:200409363 2020.
[79]
Han Z, Wei B, Hong Y, et al. Accurate screening of COVID-19 using attention-based deep 3D multiple instance learning. IEEE Trans Med Imaging 2020; 39(8): 2584-94.
[http://dx.doi.org/10.1109/TMI.2020.2996256] [PMID: 32730211]
[80]
Chen J, Wu L, Zhang J, et al. Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: A prospective study. MedRxiv 2020.
[http://dx.doi.org/10.1101/2020.02.25.20021568]
[81]
Ozkaya U, Ozturk S, Barstugan M. Coronavirus (COVID-19) classification using deep features fusion and ranking technique. arXiv preprint arXiv:200403698 2020.
[82]
Warman A, Warman P, Sharma A, et al. Interpretable artificial intelligence for COVID-19 diagnosis from chest CT reveals specificity of ground-glass opacities. medRxiv 2020.
[http://dx.doi.org/10.1101/2020.05.16.20103408]
[83]
Chassagnon G, Vakalopoulou M, Battistella E, et al. AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia. arXiv preprint arXiv:200412852 2020.
[84]
Shen C, Yu N, Cai S, et al. Quantitative computed tomography analysis for stratifying the severity of coronavirus disease 2019. J Pharm Anal 2020; 10(2): 123-9.
[http://dx.doi.org/10.1016/j.jpha.2020.03.004] [PMID: 32292624]
[85]
Tang Z, Zhao W, Xie X, et al. Severity assessment of coronavirus disease 2019 (COVID-19) using quantitative features from chest CT images. arXiv preprint arXiv:200311988 2020.
[86]
Chaganti S, Grenier P, Balachandran A, et al. Automated quantification of CT patterns associated with COVID-19 from chest CT. Radiol Artif Intell 2020; 2(4): e200048.
[http://dx.doi.org/10.1148/ryai.2020200048] [PMID: 33928255]
[87]
Goncharov M, Pisov M, Shevtsov A, et al. CT-based COVID-19 triage: Deep multitask learning improves joint identification and severity quantification. arXiv preprint arXiv:200601441 2020.
[88]
Li MD, Arun NT, Gidwani M, et al. Automated assessment of COVID-19 pulmonary disease severity on chest radiographs using convolutional Siamese neural networks. medRxiv 2020.
[http://dx.doi.org/10.1101/2020.05.20.20108159]
[89]
Li MD, Arun NT, Gidwani M, et al. Automated assessment of COVID-19 pulmonary disease severity on chest radiographs using convolutional Siamese neural networks. medRxiv 2020.
[http://dx.doi.org/10.1101/2020.05.20.20108159]

Rights & Permissions Print Cite
Article Metrics
40
1
Wayfinder Image
© 2024 Bentham Science Publishers | Privacy Policy