Topic · COVID-19 diagnosis using AI
Most cited papers in COVID-19 diagnosis using AI
What are the most cited papers in COVID-19 diagnosis using AI?
The most cited paper in COVID-19 diagnosis using AI is "La certeza de lo impredecible: Cultura Educación y Sociedad en tiempos de COVID19" (Martínez González, Marina, 2020), with 19,306 citations. Next come "A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)" (14,399) and "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning" (12,801).
Showing 50 of 100 papers. Every paper with more than 1,315 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | La certeza de lo impredecible: Cultura Educación y Sociedad en tiempos de COVID19 Martínez González, Marina · DSPACE System - Metalibrary (University of the Coast) | 2020 | 19,306 |
| 2 | A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper) Khanam, Zeba, Achari, Vejey Pradeep Suresh, Boukhennoufa, Issam et al. · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) | 2024 | 14,399 |
| 3 | Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke et al. · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) | 12 Feb 2017 | 12,801 |
| 4 | UNet++: A Nested U-Net Architecture for Medical Image Segmentation Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh et al. · Lecture notes in computer science | 2018 | 9,545 |
| 5 | Review of deep learning: concepts, CNN architectures, challenges, applications, future directions Laith H. Alzubaidi, Jinglan Zhang, Amjad J. Humaidi et al. · Journal Of Big Data | 31 Mar 2021 | 7,920 |
| 6 | scikit-image: image processing in Python Stéfan van der Walt, Johannes L. Schönberger, Juan Nunez-Iglesias et al. · PeerJ | 19 Jun 2014 | 7,267 |
| 7 | World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19) Catrin Sohrabi, Zaid Alsafi, Niamh O’Neill et al. · International Journal of Surgery | 26 Feb 2020 | 6,329 |
| 8 | Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases Tao Ai, Zhenlu Yang, Hongyan Hou et al. · Radiology | 26 Feb 2020 | 6,035 |
| 9 | Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning Hoo-Chang Shin, Holger R. Roth, Mingchen Gao et al. · IEEE Transactions on Medical Imaging | 11 Feb 2016 | 5,901 |
| 10 | A guide to deep learning in healthcare Andre Esteva, Alexandre Robicquet, Bharath Ramsundar et al. · Nature Medicine | 24 Dec 2018 | 5,244 |
| 11 | SARS-CoV-2 Viral Load in Upper Respiratory Specimens of Infected Patients Lirong Zou, Feng Ruan, Mingxing Huang et al. · New England Journal of Medicine | 20 Feb 2020 | 5,210 |
| 12 | Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning Daniel Kermany et al. · Cell | Feb 2018 | 4,903 |
| 13 | GhostNet: More Features From Cheap Operations Kai Han, Yunhe Wang, Qi Chuan Tian et al. | Jun 2020 | 4,770 |
| 14 | Convolutional neural networks: an overview and application in radiology Rikiya Yamashita, Mizuho Nishio, Richard Kinh Gian et al. · Insights into Imaging | 22 Jun 2018 | 4,717 |
| 15 | UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh et al. · IEEE Transactions on Medical Imaging | 13 Dec 2019 | 4,396 |
| 16 | Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression Zhaohui Zheng, Ping Wang, Wei Liu et al. · Proceedings of the AAAI Conference on Artificial Intelligence | 3 Apr 2020 | 4,237 |
| 17 | YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information Chien-Yao Wang, I-Hau Yeh, Hong-Yuan Mark Liao · Lecture notes in computer science | 25 Oct 2024 | 4,075 |
| 18 | Swin-Unet: Unet-Like Pure Transformer for Medical Image Segmentation Hu Cao, Yueyue Wang, Joy Chen et al. · Lecture notes in computer science | 2023 | 4,036 |
| 19 | The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health — The latest 2019 novel coronavirus outbreak in Wuhan, China David S.C. Hui, Esam I. Azhar, Tariq A. Madani et al. · International Journal of Infectious Diseases | 14 Jan 2020 | 4,033 |
| 20 | A Review of Coronavirus Disease-2019 (COVID-19) Tanu Singhal · The Indian Journal of Pediatrics | 12 Mar 2020 | 3,680 |
| 21 | Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR Yicheng Fang, Huangqi Zhang, Jicheng Xie et al. · Radiology | 19 Feb 2020 | 3,589 |
| 22 | ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases Xiaosong Wang, Yifan Peng, Le Lu et al. | Jul 2017 | 3,497 |
| 23 | Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal Laure Wynants et al. · BMJ | 7 Apr 2020 | 3,312 |
| 24 | Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? Nima Tajbakhsh, Jae Y. Shin, Suryakanth Gurudu et al. · IEEE Transactions on Medical Imaging | 7 Mar 2016 | 3,309 |
| 25 | COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images Linda Wang, Zhong Qiu Lin, Alexander M. Wong · Scientific Reports | 11 Nov 2020 | 3,190 |
| 26 | UNETR: Transformers for 3D Medical Image Segmentation Ali Hatamizadeh, Yucheng Tang, Vishwesh Nath et al. · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) | 2022 | 3,181 |
| 27 | Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19) Feng Wen Pan, Tianhe Ye, Peng Sun et al. · Radiology | 13 Feb 2020 | 2,999 |
| 28 | ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns Malik Sallam · Healthcare | 19 Mar 2023 | 2,928 |
| 29 | AI in health and medicine Pranav Rajpurkar, Emma Chen, Oishi Banerjee et al. · Nature Medicine | 2022 | 2,914 |
| 30 | CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV) Michael S Chung, Adam Bernheim, Xueyan Mei et al. · Radiology | 4 Feb 2020 | 2,914 |
| 31 | The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans Samuel G. Armato et al. · Medical Physics | 24 Jan 2011 | 2,896 |
| 32 | Class-Balanced Loss Based on Effective Number of Samples Yin Cui, Menglin Jia, Tsung-Yi Lin et al. | Jun 2019 | 2,842 |
| 33 | Segment anything in medical images Jun Ma, Yuting He, Feifei Li et al. · Nature Communications | 22 Jan 2024 | 2,830 |
| 34 | Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection Adam Bernheim, Xueyan Mei, Mingqian Huang et al. · Radiology | 20 Feb 2020 | 2,792 |
| 35 | CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison Jeremy Irvin, Pranav Rajpurkar, Michael Kwan-Lung Ko et al. · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) | 17 Jul 2019 | 2,717 |
| 36 | Automated detection of COVID-19 cases using deep neural networks with X-ray images Tülin Öztürk, Muhammed Talo, Eylul Azra Yildirim et al. · Computers in Biology and Medicine | 28 Apr 2020 | 2,702 |
| 37 | The Parable of Google Flu: Traps in Big Data Analysis David M. J. Lazer, Ryan Kennedy, Gary King et al. · Science | 13 Mar 2014 | 2,577 |
| 38 | Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions Iqbal H. Sarker · SN Computer Science | 18 Aug 2021 | 2,558 |
| 39 | Transmission routes of 2019-nCoV and controls in dental practice Xian Peng, Xin Feng Xu, Yuqing Li et al. · International Journal of Oral Science | 3 Mar 2020 | 2,509 |
| 40 | ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope Partha Pratim Ray · Internet of Things and Cyber-Physical Systems | 2023 | 2,456 |
| 41 | Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks Ioannis D. Apostolopoulos, Tzani A. Mpesiana · Physical and Engineering Sciences in Medicine | 3 Apr 2020 | 2,435 |
| 42 | A Literature Review on Impact of COVID-19 Pandemic on Teaching and Learning Sumitra Pokhrel, Roshan Chhetri · Higher Education for the Future | 2021 | 2,426 |
| 43 | Maximum Classifier Discrepancy for Unsupervised Domain Adaptation Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku et al. | Jun 2018 | 2,291 |
| 44 | Deep visual domain adaptation: A survey Mei Wang, Weihong Deng · Neurocomputing | 29 May 2018 | 2,244 |
| 45 | End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography Diego Ardila, Atilla P. Kiraly, Sujeeth Bharadwaj et al. · Nature Medicine | 20 May 2019 | 2,191 |
| 46 | Coronavirus Disease 2019 (COVID-19): A Perspective from China Ziyue Zu, Meng Jiang, Peng Xu et al. · Radiology | 21 Feb 2020 | 2,158 |
| 47 | Chest CT for Typical Coronavirus Disease 2019 (COVID-19) Pneumonia: Relationship to Negative RT-PCR Testing Xingzhi Xie, Zheng Zhong, Wei Zhao et al. · Radiology | 12 Feb 2020 | 2,073 |
| 48 | An overview of deep learning in medical imaging focusing on MRI Alexander Selvikvåg Lundervold, Alexander Selvikvåg Lundervold, Arvid Lundervold et al. · Zeitschrift für Medizinische Physik | 13 Dec 2018 | 2,049 |
| 49 | Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy Lin Li, Lixin Qin, Zeguo Xu et al. · Radiology | 19 Mar 2020 | 2,032 |
| 50 | Consistent Detection of 2019 Novel Coronavirus in Saliva Kelvin Kai‐Wang To, Owen Tak-Yin Tsang, Cyril Chik‐Yan Yip et al. · Clinical Infectious Diseases | 10 Feb 2020 | 2,005 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.