Comput Biol Med . Deep learning for diagnosis of COVID-19 using 3D CT scans

[tetano]

Comput Biol Med


. 2021 Mar 10;132:104306.
doi: 10.1016/j.compbiomed.2021.104306. Online ahead of print.
Deep learning for diagnosis of COVID-19 using 3D CT scans


Sertan Serte[SUP] 1 [/SUP], Hasan Demirel[SUP] 2 [/SUP]



Affiliations

• PMID: 33780867
• DOI: 10.1016/j.compbiomed.2021.104306

Abstract

A new pneumonia-type coronavirus, COVID-19, recently emerged in Wuhan, China. COVID-19 has subsequently infected many people and caused many deaths worldwide. Isolating infected people is one of the methods of preventing the spread of this virus. CT scans provide detailed imaging of the lungs and assist radiologists in diagnosing COVID-19 in hospitals. However, a person's CT scan contains hundreds of slides, and the diagnosis of COVID-19 using such scans can lead to delays in hospitals. Artificial intelligence techniques could assist radiologists with rapidly and accurately detecting COVID-19 infection from these scans. This paper proposes an artificial intelligence (AI) approach to classify COVID-19 and normal CT volumes. The proposed AI method uses the ResNet-50 deep learning model to predict COVID-19 on each CT image of a 3D CT scan. Then, this AI method fuses image-level predictions to diagnose COVID-19 on a 3D CT volume. We show that the proposed deep learning model provides 96% AUC value for detecting COVID-19 on CT scans.

Keywords: COVID-19; CT image; CT scan; Convolutional neural networks; Deep learning; Fusion.