Performance of a computer aided diagnosis system for SARS-CoV-2 pneumonia based on ultrasound images.

ABSTRACT:

Purpose

In this study we aimed to leverage deep learning to develop a computer aided diagnosis (CAD) system toward helping radiologists in the diagnosis of SARS-CoV-2 virus syndrome on Lung ultrasonography (LUS).

Method

A CAD system is developed based on a transfer learning of a residual network (ResNet) to extract features on LUS and help radiologists to distinguish SARS-CoV-2 virus syndrome from healthy and non-SARS-CoV-2 pneumonia. A publicly available LUS dataset for SARS-CoV-2 virus syndrome consisting of 3909 images has been employed. Six radiologists with different experiences participated in the experiment. A comprehensive LUS data set was constructed and employed to train and verify the proposed method. Several metrics such as accuracy, recall, precision, and F1-sco

SUBMITTER: Shang S 

PROVIDER: S-EPMC8609670 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature