mlsanityData to 14 Oct 2026

CoVScreen: Pitfalls and recommendations for screening COVID-19 using Chest X-rays

arXiv 2405.07674 ↗First posted 13 May 2024eess.IV cs.CVcited by 0

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Abstract (arXiv)

The novel coronavirus (COVID-19), a highly infectious respiratory disease caused by the SARS-CoV-2 has emerged as an unprecedented healthcare crisis. The pandemic had a devastating impact on the health, well-being, and economy of the global population. Early screening and diagnosis of symptomatic patients plays crucial role in isolation of patient to help stop community transmission as well as providing early treatment helping in reducing the mortality rate. Although, the RT-PCR test is the gold standard for COVID-19 testing, it is a manual, laborious, time consuming, uncomfortable, and invasive process. Due to its accessibility, availability, lower-cost, ease of sanitisation, and portable setup, chest X-Ray imaging can serve as an effective screening and diagnostic tool. In this study, we first highlight limitations of existing datasets and studies in terms of data quality, data imbalance, and evaluation strategy. Second, we curated a large-scale COVID-19 chest X-ray dataset from many publicly available COVID-19 imaging databases and proposed a pre-processing pipeline to improve quality of the dataset. We proposed CoVScreen, an CNN architecture to train and test the curated dataset. The experimental results applying different classification scenarios on the curated dataset in terms of various evaluation metrics demonstrate the effectiveness of proposed methodology in the screening of COVID-19 infection.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 64 references that are in the map. First posted on arXiv 13 May 2024.

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18 of 64 references.

References · 18

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2016Densely Connected Convolutional Networkscited by 6,720
  3. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,423
  4. 2017ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseasescited by 972
  5. 2019PadChest: A large chest x-ray image dataset with multi-label annotated reportscited by 309
  6. 2020COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Imagescited by 264
  7. 2020COVID-19 Image Data Collectioncited by 101
  8. 2020Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networkscited by 92
  9. 2020Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networkscited by 86
  10. 2020BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patientscited by 82
  11. 2020CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray imagescited by 68
  12. 2020COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Imagescited by 60
6 more
  1. 2020Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detectioncited by 46
  2. 2020COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on Chest X-Ray imagescited by 32
  3. 2020COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographscited by 28
  4. 2020Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networkscited by 14
  5. 2019Targeted transfer learning to improve performance in small medical physics datasetscited by 11
  6. 2020Detection of Coronavirus (COVID-19) Associated Pneumonia based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model using Chest X-ray Datasetcited by 10

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