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Computed tomography-based COVID-19 triage through a deep neural network using mask-weighted global average pooling.

Hong-Tao Zhang¹, Ze-Yu Sun², Juan Zhou¹

¹Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China.

Frontiers in Cellular and Infection Microbiology

|March 20, 2023

Summary

A new deep learning method using chest CT scans effectively triages coronavirus disease 2019 (COVID-19) patients. This AI tool shows high accuracy, aiding doctors in rapid COVID-19 diagnosis and patient management.

Keywords:

artificial intelligence computed tomography (CT)coronavirus disease 2019 (COVID-19)deep learning global average pooling (GAP)

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Area of Science:

Artificial Intelligence
Medical Imaging
Radiology

Background:

Urgent need for accurate COVID-19 triage methods.
Millions of people require effective screening.
Chest CT scans are a key diagnostic tool.

Purpose of the Study:

Develop a novel deep-learning approach for COVID-19 triage.
Utilize chest computed tomography (CT) images for classification.
Differentiate between COVID-19, normal, and pneumonia cases.

Main Methods:

Collected 2,809 chest CT scans (COVID-19, normal, pneumonia).
Employed a U-net CNN for lung segmentation.
Proposed a mask-weighted global average pooling (GAP) method for classification.

Main Results:

Achieved 96.5% dice value for lung segmentation.
COVID-19 triage sensitivity of 96.5% and specificity of 87.8%.
Mask-weighted GAP improved sensitivity by 0.9% and specificity by 2% over normal GAP.

Conclusions:

Proposed mask-weighted GAP deep learning method shows promise for COVID-19 triage.
The method is effective in classifying COVID-19 from chest CT scans.
This AI tool can assist clinicians in diagnosing COVID-19.