mlsanityData to 7 Oct 2026

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

arXiv 2003.13145 ↗First posted 29 Mar 2020cs.LG cs.CVcited by 200

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Abstract (arXiv)

Coronavirus disease (COVID-19) is a pandemic disease, which has already caused thousands of causalities and infected several millions of people worldwide. Any technological tool enabling rapid screening of the COVID-19 infection with high accuracy can be crucially helpful to healthcare professionals. The main clinical tool currently in use for the diagnosis of COVID-19 is the Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires specialized medical personnel. X-ray imaging is an easily accessible tool that can be an excellent alternative in the COVID-19 diagnosis. This research was taken to investigate the utility of artificial intelligence (AI) in the rapid and accurate detection of COVID-19 from chest X-ray images. The aim of this paper is to propose a robust technique for automatic detection of COVID-19 pneumonia from digital chest X-ray images applying pre-trained deep-learning algorithms while maximizing the detection accuracy. A public database was created by the authors combining several public databases and also by collecting images from recently published articles. The database contains a mixture of 423 COVID-19, 1485 viral pneumonia, and 1579 normal chest X-ray images. Transfer learning technique was used with the help of image augmentation to train and validate several pre-trained deep Convolutional Neural Networks (CNNs). The networks were trained to classify two different schemes: i) normal and COVID-19 pneumonia; ii) normal, viral and COVID-19 pneumonia with and without image augmentation. The classification accuracy, precision, sensitivity, and specificity for both the schemes were 99.7%, 99.7%, 99.7% and 99.55% and 97.9%, 97.95%, 97.9%, and 98.8%, respectively.

Key facts. Cited by 200 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Mar 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 35; 2022: 32; 2023: 25; 2024: 39; 2025: 37; 2026: 32.

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  1. 2023AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detectioncited by 270
  2. 2022Transformers in Medical Imaging: A Surveycited by 245
  3. 2025Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoningcited by 217
  4. 2024OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLMcited by 182
  5. 2023Federated Learning for Medical Image Analysis: A Surveycited by 77
  6. 2023Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Reviewcited by 62
  7. 2021COVID-19 Infection Localization and Severity Grading from Chest X-ray Imagescited by 46
  8. 2022Do Gradient Inversion Attacks Make Federated Learning Unsafe?cited by 44
  9. 2022Transformers in Medical Image Analysis: A Reviewcited by 41
  10. 2023Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Modelscited by 40
  11. 2024MedCLIP-SAM: Bridging Text and Image Towards Universal Medical Image Segmentationcited by 37
  12. 2024MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentationcited by 37
38 more
  1. 2025Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agentscited by 37
  2. 2021Federated Contrastive Learning for Decentralized Unlabeled Medical Imagescited by 33
  3. 2021A Systematic Collection of Medical Image Datasets for Deep Learningcited by 29
  4. 2022Should I Follow AI-based Advice? Measuring Appropriate Reliance in Human-AI Decision-Makingcited by 28
  5. 2022Spot the fake lungs: Generating Synthetic Medical Images using Neural Diffusion Modelscited by 27
  6. 2023GPT-FL: Generative Pre-trained Model-Assisted Federated Learningcited by 24
  7. 2023Federated Generative Learning with Foundation Modelscited by 23
  8. 2025MAISI-v2: Accelerated 3D High-Resolution Medical Image Synthesis with Rectified Flow and Region-specific Contrastive Losscited by 20
  9. 2022RadioTransformer: A Cascaded Global-Focal Transformer for Visual Attention-guided Disease Classificationcited by 19
  10. 2022NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Researchcited by 18
  11. 2024Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlookscited by 18
  12. 2023Large-scale Long-tailed Disease Diagnosis on Radiology Imagescited by 17
  13. 2024MedViLaM: A multimodal large language model with advanced generalizability and explainability for medical data understanding and generationcited by 17
  14. 2021Hybrid quantum convolutional neural networks model for COVID-19 prediction using chest X-Ray imagescited by 16
  15. 2021Self-supervised deep convolutional neural network for chest X-ray classificationcited by 16
  16. 2023Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and Challengescited by 16
  17. 2024M4CXR: Exploring Multi-task Potentials of Multi-modal Large Language Models for Chest X-ray Interpretationcited by 16
  18. 2024Large Language Model Benchmarks in Medical Taskscited by 16
  19. 2022Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approachescited by 15
  20. 2021Vision Transformer based COVID-19 Detection using Chest X-rayscited by 14
  21. 2024A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysiscited by 14
  22. 2022A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicroncited by 13
  23. 2024Representing Part-Whole Hierarchies in Foundation Models by Learning Localizability, Composability, and Decomposability from Anatomy via Self-Supervisioncited by 12
  24. 2024A foundation model for generalizable disease diagnosis in chest X-ray imagescited by 12
  25. 2025Enhancing SAM with Efficient Prompting and Preference Optimization for Semi-supervised Medical Image Segmentationcited by 12
  26. 2024A Survey of Medical Vision-and-Language Applications and Their Techniquescited by 11
  27. 2021Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray imagescited by 10
  28. 2021A methodology for training homomorphicencryption friendly neural networkscited by 10
  29. 2025UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretationcited by 10
  30. 2021Randomly Initialized Convolutional Neural Network for the Recognition of COVID-19 using X-ray Imagescited by 9
  31. 2021MedRDF: A Robust and Retrain-Less Diagnostic Framework for Medical Pretrained Models Against Adversarial Attackcited by 9
  32. 2022Recent Progress in Transformer-based Medical Image Analysiscited by 9
  33. 2024Towards Interpretable Radiology Report Generation via Concept Bottlenecks using a Multi-Agentic RAGcited by 9
  34. 2025Full Conformal Adaptation of Medical Vision-Language Modelscited by 9
  35. 2021COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approachcited by 8
  36. 2022An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural Networkscited by 8
  37. 2022Temporal Context Matters: Enhancing Single Image Prediction with Disease Progression Representationscited by 8
  38. 2022Interpretable CNN-Multilevel Attention Transformer for Rapid Recognition of Pneumonia from Chest X-Ray Imagescited by 8

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