COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings.

ABSTRACT:

Objectives

CT findings of COVID-19 look similar to other atypical and viral (non-COVID-19) pneumonia diseases. This study proposes a clinical computer-aided diagnosis (CAD) system using CT features to automatically discriminate COVID-19 from non-COVID-19 pneumonia patients.

Methods

Overall, 612 patients (306 COVID-19 and 306 non-COVID-19 pneumonia) were recruited. Twenty radiological features were extracted from CT images to evaluate the pattern, location, and distribution of lesions of patients in both groups. All significant CT features were fed in five classifiers namely decision tree, K-nearest neighbor, naïve Bayes, support vector machine, and ensemble to evaluate the best performing CAD system in classifying COVID-19 and non-COVID-19 cases.

Results

Location and

SUBMITTER: Abbasian Ardakani A 

PROVIDER: S-EPMC7395802 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature