mlsanityData to 14 Oct 2026

Cov3d: Detection of the presence and severity of COVID-19 from CT scans using 3D ResNets

arXiv 2207.12218 ↗First posted 5 Jul 2022eess.IV cs.CV cs.LGcited by 4

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Abstract (arXiv)

Deep learning has been used to assist in the analysis of medical imaging. One such use is the classification of Computed Tomography (CT) scans when detecting for COVID-19 in subjects. This paper presents Cov3d, a three dimensional convolutional neural network for detecting the presence and severity of COVID19 from chest CT scans. Trained on the COV19-CT-DB dataset with human expert annotations, it achieves a macro f1 score of 0.9476 on the validation set for the task of detecting the presence of COVID19. For the task of classifying the severity of COVID19, it achieves a macro f1 score of 0.7552. Both results improve on the baseline results of the `AI-enabled Medical Image Analysis Workshop and Covid-19 Diagnosis Competition' (MIA-COV19D) in 2022.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 10 of its 23 references that are in the map. First posted on arXiv 5 Jul 2022.

Builds on

10 of 23 references.

References · 10

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Cited by year

Citing papers per year: 2023: 3; 2024: 1.

4 papers.

  1. 2024SAM2CLIP2SAM: Vision Language Model for Segmentation of 3D CT Scans for Covid-19 Detectioncited by 14
  2. 2023COVID-19 Detection Using Slices Processing Techniques and a Modified Xception Classifier from Computed Tomography Imagescited by 2
  3. 2023Enhanced detection of the presence and severity of COVID-19 from CT scans using lung segmentationcited by 1
  4. 2023MIA-3DCNN: COVID-19 Detection Based on a 3D CNNcited by 0