3D CNN classification model for accurate diagnosis of coronavirus disease 2019 using computed tomography images

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Abstract

Purpose: The coronavirus disease (COVID-19) has been spreading rapidly around the world. As of August 25, 2020, 23.719 million people have been infected in many countries. The cumulative death toll exceeds 812,000. Early detection of COVID-19 is essential to provide patients with appropriate medical care and protecting uninfected people. Approach: Leveraging a large computed tomography (CT) database from 1112 patients provided by China Consortium of Chest CT Image Investigation (CC-CCII), we investigated multiple solutions in detecting COVID-19 and distinguished it from other common pneumonia (CP) and normal controls. We also compared the performance of different models for complete and segmented CT slices. In particular, we studied the effects of CT-superimposition depths into volumes on the performance of our models. Results: The results show that the optimal model can identify the COVID-19 slices with 99.76% accuracy (99.96% recall, 99.35% precision, and 99.65% F1-score). The overall performance for three-way classification obtained 99.24% accuracy and a macroaverage area under the receiver operating characteristic curve (macro-AUROC) of 0.9998. To the best of our knowledge, our method achieves the highest accuracy and recall with the largest public available COVID-19 CT dataset. Conclusions: Our model can help radiologists and physicians perform rapid diagnosis, especially when the healthcare system is overloaded.

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  1. Version published to 10.1117/1.jmi.8.s1.017502
  2. ScreenIT

    SciScore for 10.1101/2021.01.21.21249999: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

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    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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    • No protocol registration statement was detected.

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  3. Version published to 10.1101/2021.01.21.21249999 on medRxiv