mlsanityData to 7 Oct 2026

COVID Detection in Chest CTs: Improving the Baseline on COV19-CT-DB

arXiv 2107.04808 ↗First posted 10 Jul 2021eess.IV cs.CVcited by 3

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Abstract (arXiv)

The paper presents a comparative analysis of three distinct approaches based on deep learning for COVID-19 detection in chest CTs. The first approach is a volumetric one, involving 3D convolutions, while the other two approaches perform at first slice-wise classification and then aggregate the results at the volume level. The experiments are carried on the COV19-CT-DB dataset, with the aim of addressing the challenge raised by the MIA-COV19D Competition within ICCV 2021. Our best results on the validation subset reach a macro-F1 score of 0.92, which improves considerably the baseline score of 0.70 set by the organizers.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 10 of its 15 references that are in the map. First posted on arXiv 10 Jul 2021.

Builds on

10 of 15 references.

References · 10

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  10. 2020Revealing Lung Affections from CTs. A Comparative Analysis of Various Deep Learning Approaches for Dealing with Volumetric Datacited by 1

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 1.

3 papers.

  1. 2024Diff3Dformer: Leveraging Slice Sequence Diffusion for Enhanced 3D CT Classification with Transformer Networkscited by 2
  2. 2022Res-Dense Net for 3D Covid Chest CT-scan classificationcited by 0
  3. 2023Post-COVID Highlights: Challenges and Solutions of AI Techniques for Swift Identification of COVID-19cited by 0