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

TL;DR

This paper introduces Cov3d, a 3D ResNet-based deep learning model that accurately detects COVID-19 presence and severity from chest CT scans, outperforming previous benchmarks.

Contribution

The paper presents a novel 3D convolutional neural network, Cov3d, trained on a large annotated dataset, achieving state-of-the-art results in COVID-19 detection and severity classification.

Findings

Achieved macro F1 score of 0.9476 for COVID-19 detection

Achieved macro F1 score of 0.7552 for severity classification

Outperformed baseline results in the MIA-COV19D challenge

Abstract

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.