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Current Medical Imaging

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

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Review Article

A Review of COVID-19 Diagnostic Approaches in Computer Vision

Author(s): Cemil Zalluhoğlu*

Volume 19, Issue 7, 2023

Published on: 13 January, 2023

Article ID: e221222212130 Pages: 18

DOI: 10.2174/1573405619666221222161832

Price: $65

Abstract

Computer vision has proven that it can solve many problems in the field of health in recent years. Processing the data obtained from the patients provided benefits in both disease detection and follow-up and control mechanisms. Studies on the use of computer vision for COVID-19, which is one of the biggest global health problems of the past years, are increasing daily. This study includes a preliminary review of COVID-19 computer vision research conducted in recent years. This review aims to help researchers who want to work in this field.

Keywords: COVID-19, computer vision, deep learning, machine learning, CT, X-Ray, LUS.

Graphical Abstract

[1]
Chen Y, Liu Q, Guo D. Emerging coronaviruses: Genome structure, replication, and pathogenesis. J Med Virol 2020; 92(4): 418-23.
[http://dx.doi.org/10.1002/jmv.25681] [PMID: 31967327]
[2]
Ulhaq A, Born J, Khan A, Gomes DPS, Chakraborty S, Paul M. COVID-19 control by computer vision approaches: A survey. IEEE Access 2020; 8: 179437-56.
[http://dx.doi.org/10.1109/ACCESS.2020.3027685] [PMID: 34812357]
[3]
Hui DSI, Azhar E, Madani TA, et al. The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health — The latest 2019 novel coronavirus outbreak in Wuhan, China. Int J Infect Dis 2020; 91: 264-6.
[http://dx.doi.org/10.1016/j.ijid.2020.01.009] [PMID: 31953166]
[4]
Shi H, Han X, Jiang N, et al. Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. Lancet Infect Dis 2020; 20(4): 425-34.
[http://dx.doi.org/10.1016/S1473-3099(20)30086-4] [PMID: 32105637]
[5]
Ai T, Yang Z, Hou H, et al. Correlation of chest CT and RT-PCR testing for 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]
[6]
Zhang F. Application of machine learning in CT images and X-rays of COVID-19 pneumonia. Medicine (Baltimore) 2021; 100(36): e26855.
[http://dx.doi.org/10.1097/MD.0000000000026855] [PMID: 34516488]
[7]
Kassania SH, Kassanib PH, Wesolowskic MJ, Schneidera KA, Detersa R. Automatic detection of coronavirus disease (COVID-19) in X-ray and CT images: A machine learning based approach. Biocybern Biomed Eng 2021; 41(3): 867-79.
[http://dx.doi.org/10.1016/j.bbe.2021.05.013] [PMID: 34108787]
[8]
Alves AFF, Miranda JRA, Reis F, et al. Automatic algorithm for quantifying lung involvement in patients with chronic obstructive pulmonary disease, infection with SARS-CoV-2, paracoccidioidomycosis and no lung disease patients. PLoS One 2021; 16(6): e0251783.
[http://dx.doi.org/10.1371/journal.pone.0251783] [PMID: 34111131]
[9]
Li Y, Xia L. Coronavirus disease 2019 (COVID-19): Role of chest CT in diagnosis and management. AJR Am J Roentgenol 2020; 214(6): 1280-6.
[http://dx.doi.org/10.2214/AJR.20.22954] [PMID: 32130038]
[10]
Parekh M, Donuru A, Balasubramanya R, Kapur S. Review of the Chest CT Differential Diagnosis of Ground-Glass Opacities in the COVID Era. Radiology 2020; 297(3): E289-302.
[http://dx.doi.org/10.1148/radiol.2020202504] [PMID: 32633678]
[11]
George PM, Barratt SL, Condliffe R, et al. Respiratory follow-up of patients with COVID-19 pneumonia. Thorax 2020; 75(11): 1009-16.
[http://dx.doi.org/10.1136/thoraxjnl-2020-215314] [PMID: 32839287]
[12]
Wang S, Kang B, Ma J, et al. A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19). Eur Radiol 2021; 31(8): 6096-104.
[http://dx.doi.org/10.1007/s00330-021-07715-1] [PMID: 33629156]
[13]
Xu X, Jiang X, Ma C, et al. A deep learning system to screen novel coronavirus disease 2019 pneumonia. Engineering (Beijing) 2020; 6(10): 1122-9.
[http://dx.doi.org/10.1016/j.eng.2020.04.010] [PMID: 32837749]
[14]
He K, Zhang X, Ren S, Sun J. Identity mappings in deep residual networks. Computer Vision – ECCV 2016; 2016: 630-45.
[http://dx.doi.org/10.1007/978-3-319-46493-0_38]
[15]
Song Y, Zheng S, Li L, et al. Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. IEEE/ACM Trans Comput Biol Bioinformatics 2021; 18(6): 2775-80.
[http://dx.doi.org/10.1109/TCBB.2021.3065361] [PMID: 33705321]
[16]
Bradski G. The OpenCV Library. Dr Dobbs J Softw Tools Prof Program 2000.
[17]
Lin T-Y, Dollár P, Girshick RB, He K, Hariharan B, Belongie SJ. Feature Pyramid Networks for Object Detection CVPR. IEEE Computer Society 2017; pp. 936-44.
[http://dx.doi.org/10.1109/CVPR.2017.106]
[18]
Fu J, Zheng H, Mei T. Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition. CVPR IEEE Computer Society. 2017; pp. 4476-84.
[19]
Jin C, Chen W, Cao Y, et al. Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nat Commun 2020; 11(1): 5088.
[http://dx.doi.org/10.1038/s41467-020-18685-1] [PMID: 33037212]
[20]
Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. MICCAI 2015; 2015: 234-41.
[21]
Li L, Qin L, Xu Z, et al. Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: Evaluation of the diagnostic accuracy. Radiology 2020; 296(2): E65-71.
[http://dx.doi.org/10.1148/radiol.2020200905] [PMID: 32191588]
[22]
Saha M, Amin SB, Sharma A, Kumar TKS, Kalia RK. AI-driven quantification of ground glass opacities in lungs of COVID-19 patients using 3D computed tomography imaging. PLoS One 2022; 17(3): e0263916.
[http://dx.doi.org/10.1371/journal.pone.0263916] [PMID: 35286309]
[23]
Qi CR, Yi L, Su H, Guibas LJ. PointNet++: Deep hierarchical feature learning on point sets in a metric space. arxiv 2017; 2017: 706.02413.
[24]
Chen J, Wu L, Zhang J, et al. Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography. Sci Rep 2020; 10(1): 19196.
[http://dx.doi.org/10.1038/s41598-020-76282-0] [PMID: 33154542]
[25]
Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J. UNet++: A Nested U-Net Architecture for Medical Image Segmentation. In: Lecture Notes in Computer Science Springer. 2018; 11045: p. 3-11.
[26]
Chollet F. GitHub 2015. Available from: https://github.com/fchollet/keras
[27]
Shan F, Gao Y, Wang J, et al. Abnormal lung quantification in chest CT images of COVID‐19 patients with deep learning and its application to severity prediction. Med Phys 2021; 48(4): 1633-45.
[http://dx.doi.org/10.1002/mp.14609] [PMID: 33225476]
[28]
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]
[29]
Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM 2017; 60(6): 84-90.
[http://dx.doi.org/10.1145/3065386]
[30]
Wang B, Jin S, Yan Q, et al. AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system. Appl Soft Comput 2021; 98: 106897.
[http://dx.doi.org/10.1016/j.asoc.2020.106897] [PMID: 33199977]
[31]
Long J, Shelhamer E, Darrell T. Fully Convolutional Networks for Semantic Segmentation. arXiv 2016; 2016: 1605.06211v1.
[32]
Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. MICCAI 2016; (2): 424-32.
[33]
Chen Y, Li J, Xiao H, Jin X, Yan S, Feng J. Dual Path Networks. NIPS 2017; 2017: 4467-75.
[34]
Frid-Adar M, Amer R, Gozes O, Nassar J, Greenspan H. COVID-19 in CXR: From Detection and Severity Scoring to Patient Disease Monitoring. IEEE J Biomed Health Inform 2021; 25(6): 1892-903.
[http://dx.doi.org/10.1109/JBHI.2021.3069169] [PMID: 33769939]
[35]
Barstugan M, Ozkaya U, Ozturk S. Coronavirus (covid-19) classification using ct images by machine learning methods. arXiv 2003; 2003: 09424.
[36]
Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995; 20(3): 273-97.
[http://dx.doi.org/10.1007/BF00994018]
[37]
Li Y, Pei X, Guo Y. 3D CNN classification model for accurate diagnosis of coronavirus disease 2019 using computed tomography images. J Med Imaging (Bellingham) 2021; 8 (Suppl. 1): 017502.
[PMID: 34322573]
[38]
Wu X, Hui H, Niu M, et al. Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study. Eur J Radiol 2020; 128: 109041.
[http://dx.doi.org/10.1016/j.ejrad.2020.109041] [PMID: 32408222]
[39]
Cifci M. Deep Learning Model for Diagnosis of Corona Virus Disease from CT Images. Int J Sci Eng Res 2022; 11(4): 273-8.
[40]
Szegedy C, Ioffe S, Vanhoucke V, Alemi AA. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. AAAI Press 2017; 2017: 4278-84.
[41]
Amyar A, Modzelewski R, Li H, Ruan S. Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation. Comput Biol Med 2020; 126: 104037.
[http://dx.doi.org/10.1016/j.compbiomed.2020.104037] [PMID: 33065387]
[42]
Farid A, Selim GA, Khater HA. Novel Approach of CT Images Feature Analysis and Prediction to Screen for Corona Virus Disease (COVID-19). Int J Sci Eng Res 2020; 11(03): 1141-9.
[43]
Sousa A, Reis F, Zerbini R, Comba J, Falcao A. CNN Filter Learning from Drawn Markers for the Detection of Suggestive Signs of COVID-19 in CT Images. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021: 3169-72.
[44]
Mei X, Lee HC, Diao K, et al. Artificial intelligence–enabled rapid diagnosis of patients with COVID-19. Nat Med 2020; 26(8): 1224-8.
[http://dx.doi.org/10.1038/s41591-020-0931-3] [PMID: 32427924]
[45]
Qu J, Yang W, Yang Y, Qin L, Yan F. Infection Control for CT Equipment and Radiographers’ Personal Protection During the Coronavirus Disease (COVID-19) Outbreak in China. AJR Am J Roentgenol 2020; 215(4): 940-4.
[http://dx.doi.org/10.2214/AJR.20.23112] [PMID: 32352309]
[46]
Kanwal N, Girdhar A, Gupta S. Region based adaptive contrast enhancement of medical x-ray images. 5th International Conference on Bioinformatics and Biomedical Engineering. 10-12 May 2011; Wuhan, China.
[http://dx.doi.org/10.1109/icbbe.2011.5780221]
[47]
Eberhard JW, Koegl R, Keaveney JP. Adaptive enhancement of Xray images. Google Patents US Patent 4,942,596, 1990.
[48]
Weinstock MB, Echenique A, Russell J, et al. Chest x-ray findings in 636 ambulatory patients with covid-19 presenting to an urgent care center: a normal chest X-ray is no guarantee. J Urgent Care Med 2020; 14(7): 13-8.
[49]
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv 2015; 2015: 1409.1556.
[50]
Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. CoRR 2014; 1409: 4842.
[51]
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the Inception Architecture for Computer Vision CoRR 2015; 1512: 00567.
[52]
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely Connected Convolutional Networks. IEEE Conf Comput Vision Pattern Recogn 2017; 2017: 2261-9.
[http://dx.doi.org/10.1109/CVPR.2017.243]
[53]
Chollet F. Xception: Deep learning with depthwise separable convolutions. IEEE Comput Soc 2017; 2017: 1800-7.
[54]
Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L. MobileNetV2: Inverted residuals and linear bottlenecks. IEEE/CVF Conf Comput Vision Pattern Recogn 2018; 2018: 4510-20.
[55]
Zhang X, Zhou X, Lin M, Sun J. ShuffleNet: An extremely efficient convolutional neural network for mobile devices. IEEE/CVF Conf Comput Vision Pattern Recogn 2018; 2018: 6848-56.
[56]
Sethy PK, Behera SK. Detection of coronavirus disease (covid-19) based on deep features. Preprints 2020; 2020; 030300.
[http://dx.doi.org/10.20944/preprints202003.0300.v1]
[57]
Farooq M, Hafeez A. Covid-resnet: A deep learning framework for screening of covid19 from radiographs. arXiv 2003; 2003: 143952020.
[58]
Abbas A, Abdelsamea MM, Gaber MM. Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Appl Intell 2021; 51(2): 854-64.
[http://dx.doi.org/10.1007/s10489-020-01829-7] [PMID: 34764548]
[59]
Narin A, Kaya C, Pamuk Z. Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. Pattern Anal Appl 2021; 24(3): 1207-20.
[http://dx.doi.org/10.1007/s10044-021-00984-y] [PMID: 33994847]
[60]
Ng MY, Lee EYP, Yang J, et al. Imaging profile of the COVID-19 infection: Radiologic findings and literature review. Radiol Cardiothorac Imaging 2020; 2(1): e200034.
[http://dx.doi.org/10.1148/ryct.2020200034] [PMID: 33778547]
[61]
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]
[62]
Karar ME, Hemdan EED, Shouman MA. Cascaded deep learning classifiers for computer-aided diagnosis of COVID-19 and pneumonia diseases in X-ray scans. Complex Intell Sys 2021; 7(1): 235-47.
[http://dx.doi.org/10.1007/s40747-020-00199-4] [PMID: 34777953]
[63]
Apostolopoulos ID, Mpesiana TA. Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med 2020; 43(2): 635-40.
[http://dx.doi.org/10.1007/s13246-020-00865-4] [PMID: 32524445]
[64]
Loey M, Smarandache FM, Khalifa N. 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]
[65]
Rahimzadeh M, Attar A. A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2. Inform Med Unlocked 2020; 19: 100360.
[http://dx.doi.org/10.1016/j.imu.2020.100360] [PMID: 32501424]
[66]
Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Rajendra Acharya U. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 2020; 121: 103792.
[http://dx.doi.org/10.1016/j.compbiomed.2020.103792] [PMID: 32568675]
[67]
Redmon J, Divvala SK, Girshick RB, Farhadi A. You only look once: Unified, real-time object detection. IEEE Comput Soc 2016; 2016: 779-88.
[68]
Arellano M, Ramos O. Deep Learning Model to Identify COVID-19 Cases from Chest Radiographs. IEEE XXVII Int Conf Electron Electrical Eng Comput (INTERCON) 2020; 2020: 9220237.
[http://dx.doi.org/10.1109/INTERCON50315.2020.9220237]
[69]
Chaudhary P, Pachori R. Automatic diagnosis of COVID-19 and pneumonia using FBD method. IEEE Int Conf Bioinform Biomed (BIBM) 2020; 2020: 9313252.
[http://dx.doi.org/10.1109/BIBM49941.2020.9313252]
[70]
Ouchicha C, Ammor O, Meknassi M. CVDNet: A novel deep learning architecture for detection of coronavirus (Covid-19) from chest x-ray images. Chaos Solitons Fractals 2020; 140: 110245.
[http://dx.doi.org/10.1016/j.chaos.2020.110245] [PMID: 32921934]
[71]
Yadav G, Maheshwari S, Agarwal A. Contrast limited adaptive histogram equalization based enhancement for real time video system. International Conference on Advances in Computing, Communications and Informatics (ICACCI). Delhi, India. 2014.
[http://dx.doi.org/10.1109/ICACCI.2014.6968381]
[72]
El Asnaoui K, Chawki Y. Using X-ray images and deep learning for automated detection of coronavirus disease. J Biomol Struct Dyn 2021; 39(10): 3615-26.
[http://dx.doi.org/10.1080/07391102.2020.1767212] [PMID: 32397844]
[73]
Gaal G, Maga B, Lukacs A. Attention u-net based adversarial architectures for chest X-ray lung segmentation. arXiv 2003; 2003: 10304.
[74]
Buonsenso D, Pata D, Chiaretti A. COVID-19 outbreak: less stethoscope, more ultrasound. Lancet Respir Med 2020; 8(5): e27.
[http://dx.doi.org/10.1016/S2213-2600(20)30120-X] [PMID: 32203708]
[75]
Soldati G, Smargiassi A, Inchingolo R, et al. Is there a role for lung ultrasound during the covid-19 pandemic? J Ultrasound Med 2020; 39(7): 1459-62.
[http://dx.doi.org/10.1002/jum.15284] [PMID: 32198775]
[76]
Sippel S, Muruganandan K, Levine A, Shah S. Review article: Use of ultrasound in the developing world. Int J Emerg Med 2011; 4(1): 72.
[http://dx.doi.org/10.1186/1865-1380-4-72] [PMID: 22152055]
[77]
Lichtenstein DA, Mezière GA. Relevance of lung ultrasound in the diagnosis of acute respiratory failure: the BLUE protocol. Chest 2008; 134(1): 117-25.
[http://dx.doi.org/10.1378/chest.07-2800] [PMID: 18403664]
[78]
Amatya Y, Rupp J, Russell FM, Saunders J, Bales B, House DR. Diagnostic use of lung ultrasound compared to chest radiograph for suspected pneumonia in a resource-limited setting. Int J Emerg Med 2018; 11(1): 8.
[http://dx.doi.org/10.1186/s12245-018-0170-2] [PMID: 29527652]
[79]
Barillari A, Fioretti M. Lung ultrasound: a new tool for the emergency physician. Intern Emerg Med 2010; 5(4): 335-40.
[http://dx.doi.org/10.1007/s11739-010-0381-x] [PMID: 20443081]
[80]
Miller A. Practical approach to lung ultrasound. BJA Educ 2016; 16(2): 39-45.
[http://dx.doi.org/10.1093/bjaceaccp/mkv012]
[81]
Mojoli F, Bouhemad B, Mongodi S, Lichtenstein D. Lung ultrasound for critically Ill patients. Am J Respir Crit Care Med 2019; 199(6): 701-14.
[http://dx.doi.org/10.1164/rccm.201802-0236CI] [PMID: 30372119]
[82]
Wang G, Ji X, Xu Y, Xiang X. Lung ultrasound: a promising tool to monitor ventilator-associated pneumonia in critically ill patients. Crit Care 2016; 20(1): 320.
[http://dx.doi.org/10.1186/s13054-016-1487-y] [PMID: 27784331]
[83]
Sultan LR, Sehgal CM. A review of early experience in lung ultrasound in the diagnosis and management of COVID-19. Ultrasound Med Biol 2020; 46(9): 2530-45.
[http://dx.doi.org/10.1016/j.ultrasmedbio.2020.05.012] [PMID: 32591166]
[84]
Bouhemad B, Mongodi S, Via G, Rouquette I. Ultrasound for “lung monitoring” of ventilated patients. Anesthesiology 2015; 122(2): 437-47.
[http://dx.doi.org/10.1097/ALN.0000000000000558] [PMID: 25501898]
[85]
Mongodi S, Bouhemad B, Orlando A, et al. Modified lung ultrasound score for assessing and monitoring pulmonary aeration. Ultraschall Med 2017; 38(5): 530-7.
[http://dx.doi.org/10.1055/s-0042-120260] [PMID: 28291991]
[86]
La Salvia M, Secco G, Torti E, et al. Deep learning and lung ultrasound for Covid-19 pneumonia detection and severity classification. Comput Biol Med 2021; 136: 104742.
[http://dx.doi.org/10.1016/j.compbiomed.2021.104742] [PMID: 34388462]
[87]
Zoph B, Vasudevan V, Shlens J, Le QV. Learning transferable architectures for scalable image recognition. IEEE/CVF Conference on Computer Vision and Pattern Recognition. 18-23 June 2018; Salt Lake City, UT, USA.
[http://dx.doi.org/10.1109/CVPR.2018.00907]
[88]
Al-Jumaili S, Duru AD, U¸can ON. Covid-19 ultrasound image classification using svm based on kernels deduced from convolutional neural network. Int Sympos Multidisciplin Studies Innovat Technol (ISMSIT) 2021; 2021: 429-33.
[http://dx.doi.org/10.1109/ISMSIT52890.2021.9604551]
[89]
Panicker MR, Chen YT, Narayan KV, et al. An approach towards physics informed lung ultrasound image scoring neural network for diagnostic assistance in COVID-19. arXiv 2021; 2021: 2106.069802021.
[90]
Born J, Brändle G, Cossio M, et al. POCOVID-Net: automatic detection of COVID-19 from a new lung ultrasound imaging dataset (POCUS). arXiv 2020; 2020: 12084.
[91]
Hou D, Hou R, Hou J. Interpretable saab subspace network for COVID-19 lung ultrasound screening. IEEE Ann Ubiquit Comput Electron Mobile Commun Confe UEMCON 2020; 2020: 9298069.
[92]
Carrer L, Donini E, Marinelli D, et al. Automatic Pleural Line Extraction and COVID-19 Scoring From Lung Ultrasound Data. IEEE Trans Ultrason Ferroelectr Freq Control 2020; 67(11): 2207-17.
[http://dx.doi.org/10.1109/TUFFC.2020.3005512] [PMID: 32746195]
[93]
Liu L, Lei W, Wan X, Liu L, Luo Y, Feng C. Semi-supervised active learning for COVID-19 lung ultrasound multi-symptom classification. IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI); 09-11 November 2020; Baltimore, MD, USA.
[94]
Baloescu C, Toporek G, Kim S, et al. Automated Lung Ultrasound B-Line Assessment Using a Deep Learning Algorithm. IEEE Trans Ultrason Ferroelectr Freq Control 2020; 67(11): 2312-20.
[http://dx.doi.org/10.1109/TUFFC.2020.3002249] [PMID: 32746183]
[95]
Gare G, Schoenling A, Philip V, et al. Dense Pixel-Labeling For Reverse-Transfer And Diagnostic Learning On Lung Ultrasound For Covid-19 And Pneumonia Detection. IEEE 18th International Symposium on Biomedical Imaging (ISBI); 13-16 April 2021; Nice, France.
[96]
Wang Y, Zhang Y, He Q, Liao H, Luo J. Quantitative analysis of pleural line and B-lines in lung ultrasound images for severity assessment of COVID-19 pneumonia. IEEE Trans Ultrason Ferroelectr Freq Control 2022; 69(1): 73-83.
[http://dx.doi.org/10.1109/TUFFC.2021.3107598] [PMID: 34428140]
[97]
Sadik F, Dastider AG, Fattah SA. SpecMEn-DL: spectral mask enhancement with deep learning models to predict COVID-19 from lung ultrasound videos. Health Inf Sci Syst 2021; 9(1): 28.
[http://dx.doi.org/10.1007/s13755-021-00154-8] [PMID: 34257953]
[98]
Erfanian Ebadi S, Krishnaswamy D, Bolouri SES, et al. Automated detection of pneumonia in lung ultrasound using deep video classification for COVID-19. Informatics in Medicine Unlocked 2021; 25: 100687.
[http://dx.doi.org/10.1016/j.imu.2021.100687] [PMID: 34368420]
[99]
Diaz-Escobar J, Ordóñez-Guillén NE, Villarreal-Reyes S, et al. Deep-learning based detection of COVID-19 using lung ultrasound imagery. PLoS One 2021; 16(8): e0255886.
[http://dx.doi.org/10.1371/journal.pone.0255886] [PMID: 34388187]
[100]
Muhammad G, Shamim Hossain M. COVID-19 and Non-COVID-19 Classification using Multi-layers Fusion From Lung Ultrasound Images. Inf Fusion 2021; 72: 80-8.
[http://dx.doi.org/10.1016/j.inffus.2021.02.013] [PMID: 33649704]
[101]
Che H, Radbel J, Sunderram J, Nosher J, Patel V, Hacihaliloglu I. Multi-feature Multi-Scale CNN-Derived COVID-19 Classification from Lung Ultrasound Data. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC); 01-05 November 2021; Mexico.
[102]
Raghavi K, Krishna V. Identify and locate covid-19 point-of-care lung ultrasound markers by using deep learning technique hopfield neural network. JES 2021; 12(6): 595-9.
[103]
Awasthi N, Dayal A, Cenkeramaddi LR, Yalavarthy PK. Mini-COVIDNet: Efficient lightweight deep neural network for ultrasound based point-of-care detection of COVID-19. IEEE Trans Ultrason Ferroelectr Freq Control 2021; 68(6): 2023-37.
[http://dx.doi.org/10.1109/TUFFC.2021.3068190] [PMID: 33755565]
[104]
Barros B, Lacerda P, Albuquerque C, Conci A. Pulmonary COVID-19: Learning spatiotemporal features combining CNN and LSTM networks for lung ultrasound video classification. Sensors (Basel) 2021; 21(16): 5486.
[http://dx.doi.org/10.3390/s21165486] [PMID: 34450928]
[105]
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): 1423-1433.e11.
[http://dx.doi.org/10.1016/j.cell.2020.04.045] [PMID: 32416069]
[106]
GitHub. Actualmed covid-19 chest X-ray dataset initiative. GitHub 2022. Available from: https://github.com/agchung/Actualmed-COVID-chestxray-dataset
[107]
Ardakani AA, Kanafi AR, Acharya UR, Khadem N, Mohammadi A. Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks. Comput Biol Med 2020; 121: 103795.
[http://dx.doi.org/10.1016/j.compbiomed.2020.103795] [PMID: 32568676]
[108]
Zheng C, Deng X, Fu Q, et al. Deep learning-based detection for COVID-19 from chest CT using weak label. medrixv 2020; 2020: 20027185.
[http://dx.doi.org/10.1101/2020.03.12.20027185]
[109]
Abbasian Ardakani A, Acharya UR, Habibollahi S, Mohammadi A. COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings. Eur Radiol 2021; 31(1): 121-30.
[http://dx.doi.org/10.1007/s00330-020-07087-y] [PMID: 32740817]
[110]
Wang S, Zha Y, Li W, et al. A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. Eur Respir J 2020; 56(2): 2000775.
[http://dx.doi.org/10.1183/13993003.00775-2020] [PMID: 32444412]
[111]
Shiraishi J, Katsuragawa S, Ikezoe J, et al. Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules. AJR Am J Roentgenol 2000; 174(1): 71-4.
[http://dx.doi.org/10.2214/ajr.174.1.1740071] [PMID: 10628457]
[112]
Cohen JP, Morrison P, Dao L. Covid-19 image data collection. arXiv 2003; 2003: 2003.11597.
[113]
GitHub. Figure 1 COVID-19 Chest X-ray Dataset Initiative. GitHub 2022. Available from: https://github.com/agchung/Figure1-COVID-chestxray-dataset
[114]
Kaggle. RSNA Pneumonia Detection Challenge | Kaggle. Kagglecom 2022. Available from: https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/
[115]
Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. Proc IEEE Conf Comput Vision Pattern Recogn 2017. 2017: 2097-106.
[http://dx.doi.org/10.1109/CVPR.2017.369]
[116]
Kaggle. COVID-19 Radiography Database. Kagglecom 2022. Available from: https://www.kaggle.com/tawsifurrahman/covid19-radiography-database
[117]
Khan AI, Shah JL, Bhat MM. CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Comput Methods Programs Biomed 2020; 196: 105581.
[http://dx.doi.org/10.1016/j.cmpb.2020.105581] [PMID: 32534344]
[118]
Brunese L, Mercaldo F, Reginelli A, Santone A. Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays. Comput Methods Programs Biomed 2020; 196: 105608.
[http://dx.doi.org/10.1016/j.cmpb.2020.105608] [PMID: 32599338]
[119]
Pham TD. Classification of COVID-19 chest X-rays with deep learning: new models or fine tuning? Health Inf Sci Syst 2021; 9(1): 2.
[http://dx.doi.org/10.1007/s13755-020-00135-3] [PMID: 33235710]
[120]
Chowdhury MEH, Rahman T, Khandakar A, et al. Can AI Help in screening viral and COVID-19 pneumonia? IEEE Access 2020; 8: 132665-76.
[http://dx.doi.org/10.1109/ACCESS.2020.3010287]
[121]
Karim R, Döhmen T, Rebholz-Schuhmann D, et al. Deep-COVIDExplainer: Explainable COVID-19 diagnosis based on chest X-ray images. arXiv 2022; 2022: 2004.04582.
[122]
Sitaula C, Hossain MB. Attention-based VGG-16 model for COVID-19 chest X-ray image classification. Appl Intell 2021; 51(5): 2850-63.
[http://dx.doi.org/10.1007/s10489-020-02055-x] [PMID: 34764568]
[123]
Keles A, Keles MB, Keles A. COV19-CNNet and COV19-ResNet: Diagnostic inference engines for early detection of COVID-19. Cognit Comput 2021; 2021: 1-11.
[http://dx.doi.org/10.1007/s12559-020-09795-5] [PMID: 33425046]
[124]
Chandra TB, Verma K, Singh BK, Jain D, Netam SS. Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble. Expert Syst Appl 2021; 165: 113909.
[http://dx.doi.org/10.1016/j.eswa.2020.113909] [PMID: 32868966]
[125]
Born J, Wiedemann N, Cossio M, et al. Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis. Appl Sci (Basel) 2021; 11(2): 672.
[http://dx.doi.org/10.3390/app11020672]
[126]
Soldati G, Smargiassi A, Inchingolo R, et al. Proposal for international standardization of the use of lung ultrasound for patients with COVID ‐19. J Ultrasound Med 2020; 39(7): 1413-9.
[http://dx.doi.org/10.1002/jum.15285] [PMID: 32227492]
[127]
Roy S, Menapace W, Oei S, et al. Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound. IEEE Trans Med Imaging 2020; 39(8): 2676-87.
[http://dx.doi.org/10.1109/TMI.2020.2994459] [PMID: 32406829]
[128]
Ebadi A, Xi P, MacLean A, et al. COVIDx-US: An open-access benchmark dataset of ultrasound imaging data for AI-driven COVID-19 analytics. Front Biosci-Landmark 2022; 27(7): 198.
[http://dx.doi.org/10.31083/j.fbl2707198] [PMID: 35866396]

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