Applications of artificial intelligence in battling against covid-19: A literature review

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423

Features, Evaluation and Treatment Coronavirus (COVID-19)

Cascella, M., Rajnik, M., Cuomo, A., Dulebohn, S. C., & Napoli, R. D. (2020) · StatPearls · Cited by 3,351

COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images

Wang, L., Lin, Z. Q., & Wong, A. M. (2020) · Scientific Reports · doi:10.1038/s41598-020-76550-z · Cited by 3,228

Automated detection of COVID-19 cases using deep neural networks with X-ray images

Öztürk, T., Talo, M., Yildirim, E. A., Baloğlu, U. B., Yıldırım, Ö., & Acharya, U. R. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103792 · Cited by 2,652

Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks

Apostolopoulos, I. D., & Mpesiana, T. A. (2020) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-020-00865-4 · Cited by 2,338

Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks

Narin, A., Kaya, C., & Pamuk, Z. (2021) · Pattern Analysis and Applications · doi:10.1007/s10044-021-00984-y · Cited by 2,054

Can AI help in screening viral and COVID-19 pneumonia?

Chowdhury, M. E. H., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M. A., Mahbub, Z. B., Islam, K. R., Khan, M. S., Iqbal, A., Emadi, N. A., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3010287 · Cited by 1,913

The Impact of COVID-19 Epidemic Declaration on Psychological Consequences: A Study on Active Weibo Users

Li, S., Wang, Y., Xue, J., Zhao, N., & Zhu, T. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17062032 · Cited by 1,835

A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)

Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., Cai, M., Yang, J., Li, Y., Meng, X., et al. (2021) · European Radiology · doi:10.1007/s00330-021-07715-1 · Cited by 1,694

Artificial Intelligence (AI) applications for COVID-19 pandemic

Vaishya, R., Javaid, M., Khan, I. H., & Haleem, A. (2020) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2020.04.012 · Cited by 1,502

Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19

Shi, F., Wang, J., Shi, J., Wu, Z., Wang, Q., Tang, Z., He, K., Shi, Y., & Shen, D. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.2987975 · Cited by 1,404

Response to COVID-19 in Taiwan: Big Data Analytics, New Technology, and Proactive Testing

Wang, C. J., Ng, C. Y., & Brook, R. H. (2020) · JAMA · doi:10.1001/jama.2020.3151 · Cited by 1,391

Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions

Yang, Z., Zeng, Z., Wang, K., Wong, S., Liang, W., Zanin, M., Liu, P., Cao, X., Gao, Z., Mai, Z., et al. (2020) · Journal of Thoracic Disease · doi:10.21037/jtd.2020.02.64 · Cited by 1,389

CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images

Khan, A. I., Shah, J. L., & Bhat, M. M. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105581 · Cited by 1,351

An interpretable mortality prediction model for COVID-19 patients

Li, Y., Zhang, H., Gonçalves, J. M., Xiao, Y., Wang, M., Guo, Y., Sun, C., Tang, X., Jing, L., Zhang, M., et al. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0180-7 · Cited by 1,328

Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images

Fan, D.-P., Zhou, T., Ji, G.-P., Zhou, Y., Chen, G., Fu, H., Shen, J., & Shao, L. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2996645 · Cited by 1,273

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

(2018) · doi:10.1109/icassp34228.2018 · Cited by 1,259

A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT

Zheng, C., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., & Wang, X. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2995965 · Cited by 1,253

COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray ImagesPreprint

Hemdan, E. E., Shouman, M. A., & Karar, M. E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.11055 · Cited by 1,218

A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia

Xu, X., Jiang, X., Ma, C., Du, P., Li, X., Lv, S., Yu, L., Ni, Q., Chen, Y., Su, J., et al. (2020) · Engineering · doi:10.1016/j.eng.2020.04.010 · Cited by 1,209

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

Abbas, A., Abdelsamea, M. M., & Gaber, M. M. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01829-7 · Cited by 1,174

COVID-19 Image Data Collection: Prospective Predictions are the Future

Cohen, J., Morrison, P., Dao, L., Roth, K., Duong, T. Q., & Ghassemi, M. (2020) · The Journal of Machine Learning for Biomedical Imaging · doi:10.59275/j.melba.2020-48g7 · Cited by 1,110

A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact

Chamola, V., Hassija, V., Gupta, V., & Guizani, M. (2020) · IEEE Access · doi:10.1109/access.2020.2992341 · Cited by 1,099

Artificial intelligence–enabled rapid diagnosis of patients with COVID-19

Mei, X., Lee, H.-C., Diao, K.-Y., Huang, M., Lin, B., Liu, C., Xie, Z., Ma, Y., Robson, P. M., Chung, M., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0931-3 · Cited by 1,084

Time series forecasting of COVID-19 transmission in Canada using LSTM networks

Chimmula, V. K. R., & Zhang, L. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.109864 · Cited by 1,082

Lunar impact crater identification and age estimation with Chang’E data by deep and transfer learning

Yang, C., Zhao, H., Bruzzone, L., Benediktsson, J. A., Liang, Y., Liu, B., Zeng, X., Guan, R., Li, C., & Ouyang, Z. (2020) · Nature Communications · doi:10.1038/s41467-020-20215-y · Cited by 1,069

Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning

Minaee, S., Kafieh, R., Milan Sonka, A., Yazdani, S., & Soufi, G. J. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101794 · Cited by 1,056

A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)Preprint

Wang, S., Kang, B., Ma, J., et al. (2020) · medRxiv · doi:10.1101/2020.02.14.20023028 · Cited by 1,035

Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study

Randhawa, G. S., Soltysiak, M. P. M., El Roz, H., de Souza, C. P. E., Hill, K. A., & Kari, L. (2020) · PLOS ONE · doi:10.1371/journal.pone.0232391 · Cited by 1,023

Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images

Song, Y., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z., Chen, J., Zhao, H., Wang, R., Chong, Y., et al. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3065361 · Cited by 978

Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography

Zhang, K., Liu, X., Shen, J., Li, Z., Sang, Y., Wu, X., Zha, Y., Liang, W., Wang, C., Wang, K., et al. (2020) · Cell · doi:10.1016/j.cell.2020.04.045 · Cited by 917

Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets

Oh, Y., Park, S. J., & Ye, J. C. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2993291 · Cited by 912

Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks

Ardakani, A. A., Kanafi, A. R., Acharya, U. R., Khadem, N., & Mohammadi, A. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103795 · Cited by 864

Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review

Lalmuanawma, S., Hussain, J., & Chhakchhuak, L. (2020) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110059 · Cited by 845

Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image AnalysisPreprint

Gozes, O., Frid-Adar, M., Greenspan, H. K., Browning, P. D., Zhang, H., Ji, W., Bernheim, A., & Siegel, E. L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.05037 · Cited by 840

Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model

Beck, B. R., Shin, B., Choi, Y., Park, S., & Kang, K. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.03.025 · Cited by 815

CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection

Waheed, A., Goyal, M., Gupta, D. K., Khanna, A. K., Al-Turjman, F., & Pinheiro, P. R. (2020) · IEEE Access · doi:10.1109/access.2020.2994762 · Cited by 779

COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images

Uçar, F., & Korkmaz, D. (2020) · Medical Hypotheses · doi:10.1016/j.mehy.2020.109761 · Cited by 768

Detection of Coronavirus Disease (COVID-19) Based on Deep FeaturesPreprint

Sethy, P. K., & Behera, S. K. (2020) · Preprints.org · doi:10.20944/preprints202003.0300.v1 · Cited by 730

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

Islam, M. Z., Islam, M. M., & Asraf, A. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100412 · Cited by 729

Deep Learning-based Detection for COVID-19 from Chest CT using Weak LabelPreprint

Zheng, C., Deng, X., Fu, Q., et al. (2020) · medRxiv · doi:10.1101/2020.03.12.20027185 · Cited by 727

COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images

Afshar, P., Heidarian, S., Naderkhani, F., Oikonomou, A., Plataniotis, K. N., & Mohammadi, A. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.09.010 · Cited by 718

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

Rahimzadeh, M., & Attar, A. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100360 · Cited by 695

Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography

Chen, J., Wu, L., Zhang, J., Zhang, L., Gong, D., Zhao, Y., Chen, Q., Huang, S., Yang, M., Yang, X., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-76282-0 · Cited by 685

Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks

Singh, D., Kumar, V., Vaishali, & Kaur, M. (2020) · European Journal of Clinical Microbiology & Infectious Diseases · doi:10.1007/s10096-020-03901-z · Cited by 658

Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet

Panwar, H., Gupta, P. K., Siddiqui, M. K., Morales-Menéndez, R., & Singh, V. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.109944 · Cited by 645

Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning

Jaiswal, A., Gianchandani, N., Singh, D., Kumar, V., & Kaur, M. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1788642 · Cited by 632

Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation

Amyar, A., Modzelewski, R., Li, H., & Ruan, S. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104037 · Cited by 626

Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays

Brunese, L., Mercaldo, F., Reginelli, A., & Santone, A. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105608 · Cited by 623

AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app

Imran, A., Posokhova, I., Qureshi, H. N., Masood, U., Riaz, M. S., Ali, K., John, C. N., Hussain, M. I., & Nabeel, M. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100378 · Cited by 621

Development and evaluation of an artificial intelligence system for COVID-19 diagnosis

Jin, C., Chen, W., Cao, Y., Xu, Z., Tan, Z., Zhang, X., Deng, L., Zheng, C., Zhou, J., Shi, H., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18685-1 · Cited by 611

Artificial intelligence and machine learning to fight COVID-19

Alimadadi, A., Aryal, S., Manandhar, I., Munroe, P. B., Joe, B., & Cheng, X. (2020) · Physiological Genomics · doi:10.1152/physiolgenomics.00029.2020 · Cited by 609

Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound

Roy, S., Menapace, W., Oei, S. P., Luijten, B., Fini, E., Saltori, C., Huijben, I. A. M., Chennakeshava, N., Mento, F., Sentelli, A., et al. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2994459 · Cited by 597

Analysis of User Satisfaction with Online Education Platforms in China during the COVID-19 Pandemic

Chen, T., Peng, L., Yin, X., Rong, J., Yang, J., & Cong, G. (2020) · Healthcare · doi:10.3390/healthcare8030200 · Cited by 594

COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches

Toğaçar, M., Ergen, B., & Cömert, Z. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103805 · Cited by 574

Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment

Jamshidi, M., Lalbakhsh, A., Talla, J., Peroutka, Z., Hadjilooei, F., Lalbakhsh, P., Jamshidi, M., Spada, L. L., Mirmozafari, M., Dehghani, M., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3001973 · Cited by 551

AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system

Wang, B., Jin, S., Yan, Q., Xu, H., Luo, C. W., Wei, L., Zhao, W. Z., Hou, X., Ma, W., Xu, Z., et al. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106897 · Cited by 548

Machine learning-based prediction of COVID-19 diagnosis based on symptoms

Zoabi, Y., Deri-Rozov, S., & Shomron, N. (2021) · npj Digital Medicine · doi:10.1038/s41746-020-00372-6 · Cited by 540

Within the lack of chest COVID-19 X-ray dataset: A novel detection model based on GAN and deep transfer learning

Loey, M., Smarandache, F., & M. Khalifa, N. E. (2020) · Symmetry · doi:10.3390/sym12040651 · Cited by 538

Misinformation sharing and social media fatigue during COVID-19: an affordance and cognitive load perspective.

Islam, A. K. M. N., Laato, S., Talukder, S., & Sutinen, E. (2020) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2020.120201 · Cited by 538

COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm

Iwendi, C., Bashir, A. K., Peshkar, A., Sujatha, R., Chatterjee, J. M., Pasupuleti, S., Mishra, R., Pillai, S. K., & Jo, O. (2020) · Frontiers in Public Health · doi:10.3389/fpubh.2020.00357 · Cited by 536

Mapping the landscape of Artificial Intelligence applications against COVID-19

Aylett-Bullock, J., Luccioni, A. S., Pham, K. H., Lam, C. S. N., & Luengo-Oroz, M. A. (2020) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12162 · Cited by 533

Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study

Zeroual, A., Harrou, F., Dairi, A., & Sun, Y. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.110121 · Cited by 531

COVID-19 Future Forecasting Using Supervised Machine Learning Models

Rustam, F., Reshi, A. A., Mehmood, A., Ullah, S., On, B.-W., Aslam, W., & Choi, G. S. (2020) · IEEE Access · doi:10.1109/access.2020.2997311 · Cited by 531

COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios

Pereira, R. M., Bertolini, D. S., Teixeira, L., Junior, C. N. S., & Costa, Y. M. G. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105532 · Cited by 527

CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization

Mahmud, T., Rahman, M. A., & Fattah, S. A. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103869 · Cited by 525

JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation

Wu, Y.-H., Gao, S.-H., Mei, J., Xu, J., Fan, D.-P., Zhang, R.-G., & Cheng, M.-M. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3058783 · Cited by 516

COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from RadiographsPreprint

Farooq, M. A., & Hafeez, A. B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.14395 · Cited by 506

Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing

Tuli, S., Tuli, S., Tuli, R., & Gill, S. S. (2020) · Internet of Things · doi:10.1016/j.iot.2020.100222 · Cited by 504

COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning

Ong, E., Wong, M. U., Huffman, A., & He, Y. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.01581 · Cited by 499

A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis

Wang, S., Zha, Y., Li, W., Wu, Q., Li, X., Niu, M., Wang, M., Qiu, X., Li, H., He, Y., et al. (2020) · European Respiratory Journal · doi:10.1183/13993003.00775-2020 · Cited by 493

Lung Infection Quantification of COVID-19 in CT Images with Deep LearningPreprint

Shan, F., Gao, Y., Wang, J., Shi, W., Shi, N., Han, M., Xue, Z., Shen, D. G., & Shi, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.04655 · Cited by 489

Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil

Ribeiro, M. H. D. M., da Silva, R. G., Mariani, V. C., & Coelho, L. D. S. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.109853 · Cited by 475

Deep Transfer Learning Based Classification Model for COVID-19 Disease

Pathak, Y., Shukla, P. K., Tiwari, A., Stalin, S., Singh, S., & Shukla, P. K. (2020) · IRBM · doi:10.1016/j.irbm.2020.05.003 · Cited by 462

Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity

Jiang, X., Coffee, M. P., Bari, A., Wang, J., Jiang, X., Huang, J., Shi, J., Dai, J., Cai, J., Zhang, T., et al. (2020) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2020.010691 · Cited by 454

On the Coronavirus (COVID-19) Outbreak and the Smart City Network: Universal Data Sharing Standards Coupled with Artificial Intelligence (AI) to Benefit Urban Health Monitoring and Management

Allam, Z., & Jones, D. S. (2020) · Healthcare · doi:10.3390/healthcare8010046 · Cited by 450

Coronavirus (COVID-19) Classification using CT Images by Machine Learning MethodsPreprint

Barstuğan, M., Özkaya, U., & Öztürk, Ş. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.09424 · Cited by 449

Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data

Brown, C., Chauhan, J., Grammenos, A., Han, J., Hasthanasombat, A., Spathis, D., Xia, T., Cicuta, P., & Mascolo, C. (2020) · Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3394486.3412865 · Cited by 447

Prediction for the spread of COVID-19 in India and effectiveness of preventive measures

Tomar, A., & Gupta, N. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.138762 · Cited by 446

Optimization Method for Forecasting Confirmed Cases of COVID-19 in China

Al‐qaness, M. A. A., Ewees, A. A., Fan, H. M., & Elaziz, M. A. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9030674 · Cited by 441

Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT ScansPreprint

He, X., Yang, X., Zhang, S., Zhao, J., Zhang, Y., Xing, E., & Xie, P. (2020) · medRxiv · doi:10.1101/2020.04.13.20063941 · Cited by 439

Serial Quantitative Chest CT Assessment of COVID-19: A Deep Learning Approach

Huang, L., Han, R., Ai, T., Yu, P., Kang, H. S., Tao, Q., & Xia, L. (2020) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.2020200075 · Cited by 429

COVID-19 Outbreak Prediction with Machine Learning

Ardabili, S., Mosavi, A. H., Ghamisi, P., Filip, F., Várkonyi-Kóczy, A. R., Reuter, U., Rabczuk, T., & Atkinson, P. M. (2020) · Algorithms · doi:10.3390/a13100249 · Cited by 427

A machine learning-based model for survival prediction in patients with severe COVID-19 infectionPreprint

Li, Y., Zhang, H., Gonçalves, J. M., et al. (2020) · medRxiv · doi:10.1101/2020.02.27.20028027 · Cited by 426

Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms

Heidari, M., Mirniaharikandehei, S., Khuzani, A. Z., Danala, G., Qiu, Y., & Zheng, B. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104284 · Cited by 423

Conspiracy in the time of corona: automatic detection of emerging COVID-19 conspiracy theories in social media and the news

Shahsavari, S., Holur, P., Wang, T., Tangherlini, T. R., & Roychowdhury, V. (2020) · Journal of Computational Social Science · doi:10.1007/s42001-020-00086-5 · Cited by 420

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

Jelodar, H., Wang, Y., Orji, R., & Huang, S. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3001216 · Cited by 416

Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis

Chakraborty, T., & Ghosh, I. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.109850 · Cited by 415

Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts

Pham, Q.-V., Nguyen, D. C., Huynh-The, T., Hwang, W.-J., & Pathirana, P. N. (2020) · IEEE Access · doi:10.1109/access.2020.3009328 · Cited by 413

The Role of Imaging in the Detection and Management of COVID-19: A Review

Dong, D., Tang, Z., Wang, S., Hui, H., Gong, L., Lu, Y., Xue, Z., Liao, H., Chen, F., Yang, F., et al. (2020) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.2990959 · Cited by 412

Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach

Kassani, S. H., Kassani, P. H., Wesolowskic, M. J., Schneidera, K. A., & Detersa, R. (2021) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2021.05.013 · Cited by 411

New machine learning method for image-based diagnosis of COVID-19

Elaziz, M. A., Hosny, K. M., Salah, A., Darwish, M. M., Lu, S., & Sahlol, A. T. (2020) · PLoS ONE · doi:10.1371/journal.pone.0235187 · Cited by 408

COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

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Unveiling COVID-19 from CHEST X-ray with deep learning: A hurdles race with small data

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COVID-Classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest X-ray images

Khuzani, A. Z., Heidari, M., & Shariati, S. A. (2021) · Scientific Reports · doi:10.1038/s41598-021-88807-2 · Cited by 210

Application of Artificial Intelligence in COVID-19 drug repurposing

Mohanty, S., Rashid, M. H. A., Mridul, M., Mohanty, C., & Swayamsiddha, S. (2020) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2020.06.068 · Cited by 209

Medbot: Conversational Artificial Intelligence Powered Chatbot for Delivering Tele-Health after COVID-19

Bharti, U., Bajaj, D., Batra, H., Lalit, S., Lalit, S., & Gangwani, A. (2020) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/icces48766.2020.9137944 · Cited by 209

Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections

Sedik, A., Iliyasu, A. M., El-Rahiem, B. A., Samea, M. E. A., Abdel-Raheem, A., Hammad, M. A., Peng, J., El-Samie, F. E. A., & El‐Latif, A. A. A. (2020) · Viruses · doi:10.3390/v12070769 · Cited by 207

Modeling the Spread of COVID-19 Infection Using a Multilayer Perceptron

Car, Z., Šegota, S. B., Anđelić, N., Lorencin, I., & Mrzljak, V. (2020) · Computational and Mathematical Methods in Medicine · doi:10.1155/2020/5714714 · Cited by 206

COVID-19 Control by Computer Vision Approaches: A Survey

Ulhaq, A., Born, J., Khan, A. A., Gomes, D. P. S., Chakraborty, S., & Paul, M. (2020) · IEEE Access · doi:10.1109/access.2020.3027685 · Cited by 203

Coronavirus (COVID-19) Classification Using Deep Features Fusion and Ranking Technique

Özkaya, U., Öztürk, Ş., & Barstuğan, M. (2020) · Studies in big data · doi:10.1007/978-3-030-55258-9_17 · Cited by 202

Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods

Mohammed, M. A., Abdulkareem, K. H., Al‐Waisy, A. S., Mostafa, S. A., Al-Fahdawi, S., Dinar, A. M., Alhakami, W., Baz, A., Al‐Mhiqani, M. N., Alhakami, H., et al. (2020) · IEEE Access · doi:10.1109/access.2020.2995597 · Cited by 201

COVID-CXNet: Detecting COVID-19 in frontal chest X-ray images using deep learning

Haghanifar, A., Molahasani, M., Choi, Y., Deivalakshmi, S., & Ko, S. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12156-z · Cited by 200

COVID-19 diagnosis prediction in emergency care patients: a machine learning approachPreprint

Batista, A. F. D. M., Miraglia, J. L., Donato, T. H. R., & Filho, A. D. P. C. (2020) · medRxiv · doi:10.1101/2020.04.04.20052092 · Cited by 199

Data Mining and Content Analysis of the Chinese Social Media Platform Weibo During the Early COVID-19 Outbreak: Retrospective Observational Infoveillance Study

Li, J., Xu, Q., Cuomo, R. E., Purushothaman, V. L., & Mackey, T. K. (2020) · JMIR Public Health and Surveillance · doi:10.2196/18700 · Cited by 199

Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming

Salgotra, R., Gandomi, M., & Gandomi, A. H. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.109945 · Cited by 198

Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review

Swapnarekha, H., Behera, H. S., Nayak, J., & Naik, B. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.109947 · Cited by 198

Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study

An, C., Lim, H., Kim, D. W., Chang, J. H., Choi, Y. J., & Kim, S. W. (2020) · Scientific Reports · doi:10.1038/s41598-020-75767-2 · Cited by 198

Using Machine Learning to Predict ICU Transfer in Hospitalized COVID-19 Patients

Cheng, F.-Y., Joshi, H. N., Tandon, P., Freeman, R., Reich, D. L., Mazumdar, M., Kohli-Seth, R. D., Levin, M. A., Timsina, P., & Kia, A. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9061668 · Cited by 197

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906

Coronavirus disease (COVID-19) cases analysis using machine-learning applications

Kwekha-Rashid, A. S., Abduljabbar, H. N., & Alhayani, B. (2021) · Applied Nanoscience · doi:10.1007/s13204-021-01868-7 · Cited by 348

Machine Learning (ML) in Medicine: Review, Applications, and Challenges

Rahmani, A. M., Yousefpoor, E., Yousefpoor, M. S., Mehmood, Z., Haider, A., Hosseinzadeh, M., & Naqvi, R. A. (2021) · Mathematics · doi:10.3390/math9222970 · Cited by 289

Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review

Alballa, N., & Al-Turaiki, I. M. (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100564 · Cited by 232

Surveillance and response strategies for zoonotic diseases: a comprehensive review

Sharan, M., Vijay, D., Yadav, J. P., Bedi, J. S., & Dhaka, P. (2023) · Science in One Health · doi:10.1016/j.soh.2023.100050 · Cited by 191

Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research DirectionsPreprint

Nguyen, T. T. (2020) · Figshare · doi:10.6084/m9.figshare.12127020

A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19

Chen, J., Li, K., Zhang, Z., Li, K., & Yu, P. S. (2021) · ACM Computing Surveys · doi:10.1145/3465398 · Cited by 154

Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic

El-Rashidy, N. M., Abdelrazek, S. M., Abuhmed, T., Amer, E., Ali, F., Hu, J. W., & El–Sappagh, S. (2021) · Diagnostics · doi:10.3390/diagnostics11071155 · Cited by 62

Applications of Artificial Intelligence in Combating Covid-19: A Systematic Review

Enughwure, A. A., & Febaide, I. C. (2020) · OALib · doi:10.4236/oalib.1106628 · Cited by 17

A STUDY ON ARTIFICIAL INTELLIGENCE IN THE TIMES OF COVID-19

(2020) · International Journal for Research in Engineering Application & Management · doi:10.35291/2454-9150.2020.0469 · Cited by 1