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Research Article

Meta-analysis of predictions of COVID-19 disease based on CT-scan and X-ray images

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Reference

  • Al-Mandhari, Ahmed, et al. “Coronavirus Disease 2019 outbreak: preparedness and readiness of countries in the Eastern Mediterranean Region.” https://coronavirus.1science.com/item/6166e6044a2114454e136505bc74865c1c52e8da (2020).
  • COVID, LabCorp. “RT-PCR test EUA Summary.” Accelerated Emergency Use Authorization (EUA) Summary COVID-19 RT-PCR Test (Laboratory Corporation of America). Available online: http://www.fda.gov (accessed on 20 March 2020) (19).
  • Long, “Algorithms and Ai: Deep Learning Medical Imaging”, aidoc, (accessed 23.04.2020), 2019.
  • Radiological Society of North America. “CT provides best diagnosis for COVID-19.” ScienceDaily. ScienceDaily, 26 February 2020.
  • Davies, Huw TO, and Iain K. Crombie. What is meta-analysis?. Hayward Medical Communications, 1998.
  • Stroup, Donna F., et al. “Meta-analysis of observational studies in epidemiology: a proposal for reporting.” Jama 283.15 (2000): 2008-2012. doi: 10.1001/jama.283.15.2008
  • Song, Ying, et al. “Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images.” medRxiv (2020).
  • Ozturk, Tulin, et al. “Automated detection of COVID-19 cases using deep neural networks with X-ray images.” Computers in Biology and Medicine (2020): 103792.
  • Xu, X., et al. “Deep learning system to screen coronavirus disease 2019 pneumonia. arXiv 2020.” arXiv preprint arXiv:2002.09334.
  • Singh, Dilbag, Vijay Kumar, and Manjit Kaur. “Classification of COVID-19 patients from chest CT images using multi-objective differentialevolution–basedconvolutionalneuralnetworks.” European Journal of Clinical Microbiology & Infectious Diseases (2020): 1-11.
  • Barstugan, Mucahid, Umut Ozkaya, and Saban Ozturk. “Coronavirus (covid-19) classification using ct images by machine learning methods.” arXiv preprint arXiv:2003.09424 (2020).
  • Wang, Shuai, et al. “A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19).” MedRxiv (2020).
  • Zheng, Chuansheng, et al. “Deep learning-based detection for COVID-19 from chest CT using weak label.” medRxiv (2020).
  • Gozes, Ophir, 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:2003.05037 (2020).
  • Shan, Fei, et al. “Lung infection quantification of covid-19 in ct images with deep learning.” arXiv preprint arXiv:2003.04655 (2020).
  • Sethy, Prabira Kumar, and Santi Kumari Behera. “Detection of coronavirus disease (covid-19) based on deep features.” Preprints 2020030300 (2020): 2020.
  • Apostolopoulos, Ioannis D., and Tzani A. Mpesiana. “Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks.” Physical and Engineering Sciences in Medicine (2020): 1.
  • Rahimzadeh, Mohammad, and Abolfazl Attar. “A New Modified Deep Convolutional Neural Network for Detecting COVID-19 from X-ray Images.” arXiv preprint arXiv:2004.08052 (2020).
  • Hall, Lawrence O., etal.”Findingcovid-19 fromchestx-raysusingdeep learning on a small dataset.” arXiv preprint arXiv:2004.02060 (2020).
  • Abbas, Asmaa, Mohammed M. Abdelsamea, and Mohamed Medhat Gaber. “Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network.” arXiv preprint arXiv:2003.13815 (2020).
  • Hu, Shaoping, et al. “Weakly supervised deep learning for covid-19 infection detection and classification from ct images.” IEEE Access 8 (2020): 118869-118883. doi: 10.1109/ACCESS.2020.3005510
  • Narin, Ali, Ceren Kaya, and Ziynet Pamuk. “Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks.” arXiv preprint arXiv:2003.10849 (2020).
  • Wang, Ningwei, Hongzhe Liu, and Cheng Xu. “Deep Learning for The Detection of COVID-19 Using Transfer Learning and Model Integration.” 2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC). IEEE, 2020.
  • Islam, Md Zabirul, Md Milon Islam, and Amanullah Asraf. “A combined deep cnn-lstm network for the detection of novel coronavirus (covid-19) using x-ray images.” Informatics in Medicine Unlocked (2020): 100412.
  • Shah, Vruddhi, et al. “Diagnosis of COVID-19 using CT scan images and deep learning techniques.” medRxiv (2020).
  • Kadry, Seifedine, et al. “Development of a Machine-Learning System to Classify Lung CT Scan Images into Normal/COVID-19 Class.” arXiv preprint arXiv:2004.13122 (2020).
  • Mishra, Arnab Kumar, et al. “Identifying COVID19 from Chest CT Images: A Deep Convolutional Neural Networks Based Approach.” Journal of Healthcare Engineering 2020 (2020).
  • Chen, Xiaocong, Lina Yao, and Yu Zhang. “Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images.” arXiv preprint arXiv:2004.05645 (2020).
  • Chen, Jun, et al. “Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study.” MedRxiv (2020).
  • Yang, Shuyi, et al. “Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study.” Annals of Translational Medicine 8.7 (2020). doi: 10.21037/atm.2020.03.132
  • Ozkaya, Umut, Saban Ozturk, and Mucahid Barstugan. “Coronavirus (COVID-19) Classification using Deep Features Fusion and Ranking Technique.” arXiv preprint arXiv:2004.03698 (2020).
  • Yan, Qingsen, et al. “COVID-19 Chest CT Image Segmentation--A Deep Convolutional Neural Network Solution.” arXiv preprint arXiv:2004.10987 (2020).
  • Jaiswal, Aayush, et al. “Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning.” Journal of Biomolecular Structure and Dynamics (2020): 1-8. doi: 10.1080/07391102.2020.1788642
  • Anwar, Talha, and Seemab Zakir. “Deep learning based diagnosis of COVID-19 using chest CT-scan images.” (2020).
  • Silva, Pedro, et al. “Efficient Deep Learning Model for COVID-19 Detection in large CT images datasets: A cross-dataset analysis.” (2020).
  • Hoon, Ko, et al. “COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image.” Journal of medical Internet research.
  • Loey, Mohamed, Gunasekaran Manogaran, and Nour Eldeen M. Khalifa. “A deep transfer learning model with classical data augmentation and cgan to detect covid-19 from chest ct radiography digital images.” (2020).
  • Wang, Xinggang, et al. “A Weakly-supervised Framework for COVID-19 Classification and Lesion Localization from Chest CT.” IEEE Transactions on Medical Imaging (2020).
  • Ni, Qianqian, et al. “A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images.” European radiology (2020): 1-11.
  • Mohammed, Ahmed, et al. “Semi-supervised Network for Detection of COVID-19 in Chest CT Scans.” IEEE Access (2020).
  • Purohit, Kiran, et al. “Covid-19 detection on chest x-ray and ct scan images using multi-image augmented deep learning model.” BioRxiv (2020).
  • Alom, Md Zahangir, et al. “COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches.” arXiv preprint arXiv:2004.03747 (2020).
  • Wu, Xiangjun, et al. “Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: a multicentre study.” European Journal of Radiology (2020): 109041.
  • Maghdid, Halgurd S., et al. “Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms.” arXiv preprint arXiv:2004.00038 (2020).
  • Kumar, Rajesh, et al. “Blockchain-federated-learning and deep learning models for covid-19 detection using ct imaging.” arXiv preprint arXiv:2007.06537 (2020).
  • Sharma, Sachin. “Drawing insights from COVID-19-infected patients using CT scan images and machine learning techniques: a study on 200 patients.” Environmental Science and Pollution Research (2020): 1-9.
  • Amyar, Amine, Romain Modzelewski, and Su Ruan. “Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation.” medRxiv (2020).
  • He, Xuehai, et al. “Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans.” medRxiv (2020).
  • Dansana, Debabrata, et al. “Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm.” Soft Computing (2020): 1-9.
  • Sun, Liang, et al. “Adaptive feature selection guided deep forest for covid-19 classification with chest ct.” IEEE Journal of Biomedical and Health Informatics (2020).
  • Han, Zhongyi, et al. “Accurate Screening of COVID-19 using Attention Based Deep 3D Multiple Instance Learning.” IEEE Transactions on Medical Imaging (2020).
  • Oh, Yujin, Sangjoon Park, and Jong Chul Ye. “Deep learning covid-19 features on cxr using limited training data sets.” IEEE Transactions on Medical Imaging (2020).
  • Gour, Mahesh, and Sweta Jain. “Stacked Convolutional Neural Network for Diagnosis of COVID-19 Disease from X-ray Images.” arXiv preprint arXiv:2006.13817 (2020).
  • Luz, Eduardo José da S., et al. “Towards an Effective and Efficient Deep Learning Model for COVID-19 Patterns Detection in X-ray Images.” CoRR (2020).
  • Haghanifar, Arman, Mahdiyar Molahasani Majdabadi, and Seokbum Ko. “COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep Learning.” arXiv preprint arXiv:2006.13807 (2020).
  • Civit-Masot, Javier, et al. “Deep Learning system for COVID-19 diagnosis aid using X-ray pulmonary images.” Applied Sciences 10.13 (2020): 4640. doi: 10.3390/app10134640
  • Hammoudi, Karim, et al. “Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19.” arXiv preprint arXiv:2004.03399 (2020).
  • Das, N. Narayan, et al. “Automated deep transfer learning- based approach for detection of COVID-19 infection in chest X-rays.” IRBM (2020).
  • Duran-Lopez, Lourdes, et al. “COVID-XNet: A Custom Deep Learning System to Diagnose and Locate COVID-19 in Chest X-ray Images.” Applied Sciences 10.16 (2020): 5683. doi: 10.3390/app10165683
  • Basu, Sanhita, and Sushmita Mitra. “Deep Learning for Screening COVID-19 using Chest X-Ray Images.” arXiv preprint arXiv:2004.10507 (2020).
  • Khan, Asif Iqbal, Junaid Latief Shah, and Mohammad Mudasir Bhat. “Coronet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images.” Computer Methods and Programs in Biomedicine (2020): 105581.
  • Afshar, Parnian, et al. “Covid-caps: A capsule network-based framework for identification of covid-19 cases from x-ray images.” arXiv preprint arXiv:2004.02696 (2020).
  • Jain, Govardhan, et al. “A deep learning approach to detect Covid-19 coronavirus with X-Ray images.” Biocybernetics and Biomedical Engineering (2020).
  • Toraman, Suat, Talha Burak Alakus, and Ibrahim Turkoglu. “Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks.” Chaos, Solitons & Fractals 140 (2020): 110122. doi: 10.1016/j.chaos.2020.110122
  • Ouchicha, Chaimae, Ouafae Ammor, and Mohammed Meknassi. “CVDNet: A Novel Deep Learning Architecture for Detection of Coronavirus (Covid-19) from Chest X-Ray Images.” Chaos, Solitons & Fractals (2020): 110245.
  • Wang, Linda, and Alexander Wong. “COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images.” arXiv preprint arXiv:2003.09871 (2020).
  • Al-Timemy, Ali H., et al. “An Efficient Mixture of Deep and Machine Learning Models for COVID-19 and Tuberculosis Detection Using X-Ray Images in Resource Limited Settings.” arXiv preprint arXiv:2007.08223 (2020).
  • Panwar, Harsh, et al. “Application of Deep Learning for Fast Detection of COVID-19 in X-Rays using nCOVnet.” Chaos, Solitons & Fractals (2020): 109944.
  • Abraham, Bejoy, and Madhu S. Nair. “Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier.” Biocybernetics and Biomedical Engineering (2020).
  • Mangal, Arpan, et al. “CovidAID: COVID-19 Detection Using Chest X-Ray.” arXiv preprint arXiv:2004.09803 (2020).
  • Farooq, Muhammad, and Abdul Hafeez. “Covid-resnet: A deep learningframeworkforscreeningofcovid19 fromradiographs.” arXiv preprint arXiv:2003.14395 (2020).
  • Sharma, Vishal, and Curtis Dyreson. “COVID-19 detection using Residual Attention Network an Artificial Intelligence approach.” arXiv preprint arXiv:2006.16106 (2020).
  • Horry, Michael J., et al. “X-Ray Image based COVID-19 Detection using Pre-trained Deep Learning Models.” (2020).
  • Li, Tianyang, et al. “Robust Screening of COVID-19 from Chest X-ray via Discriminative Cost-Sensitive Learning.” arXiv preprint arXiv:2004.12592 (2020).
  • Elaziz, Mohamed Abd, et al. “New machine learning method for image-based diagnosis of COVID-19.” Plos one 15.6 (2020): e0235187. doi: 10.1371/journal.pone.0235187
  • Hemdan,EzzEl-Din,MarwaA.Shouman,andMohamedEsmailKarar. “Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images.” arXiv preprint arXiv:2003.11055 (2020)
  • The jamovi project,jamovi,(Version 1.1), [Computer Software], Retrieved from https://www.jamovi.org, 2019.
  • “R: A Language and environment for statistical computing”, R Core Team),[Computer Software], Retrieved from https://cran-project.org/, 2018.
  • Huang, Lu, et al. “Serial quantitative chest ct assessment of covid-19: Deep-learning approach.” Radiology: Cardiothoracic Imaging 2.2 (2020): e200075.
  • Song, Ying, et al. “Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images.” medRxiv (2020).
  • Toğaçar, Mesut, Burhan Ergen, and Zafer Cömert. “COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches.” Computers in Biology and Medicine (2020): 103805.
  • Singh, Vijander, et al. “Prediction of COVID-19 corona virus pandemic based on time series data using Support Vector Machine.” Journal of Discrete Mathematical Sciences & Cryptography (2020).
  • Bhatnagar, Vaibhav, et al. “Descriptive analysis of COVID-19 patients in the context of India.” Journal of Interdisciplinary Mathematics (2020): 1-16. doi: 10.1080/09720502.2020.1761635
  • R. Kumari, S. Kumar, R. C. Poonia, V. Singh, L. Raja, V. Bhatnagar, and P. Agarwal, Analysis and Predictions of Spread, Recovery, and Death Caused by COVID-19 in India, Big Data Mining and Analytics, IEEE, 2020. DOI: 10.26599/BDMA.2020.9020013
  • Usaini, Salisu, et al. “Modeling the transmission dynamics of the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) with latent immigrants.” Journal of Interdisciplinary Mathematics 22.6 (2019): 903-930. doi: 10.1080/09720502.2019.1692429

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