Deep Radiomic Analysis for Predicting Coronavirus Disease 2019 in Computerized Tomography and X-Ray Images.
IEEE Trans Neural Netw Learn Syst
; 33(1): 3-11, 2022 01.
Article
in English
| MEDLINE | ID: covidwho-1476080
ABSTRACT
This article proposes to encode the distribution of features learned from a convolutional neural network (CNN) using a Gaussian mixture model (GMM). These parametric features, called GMM-CNN, are derived from chest computed tomography (CT) and X-ray scans of patients with coronavirus disease 2019 (COVID-19). We use the proposed GMM-CNN features as input to a robust classifier based on random forests (RFs) to differentiate between COVID-19 and other pneumonia cases. Our experiments assess the advantage of GMM-CNN features compared with standard CNN classification on test images. Using an RF classifier (80% samples for training; 20% samples for testing), GMM-CNN features encoded with two mixture components provided a significantly better performance than standard CNN classification ( ). Specifically, our method achieved an accuracy in the range of 96.00%-96.70% and an area under the receiver operator characteristic (ROC) curve in the range of 99.29%-99.45%, with the best performance obtained by combining GMM-CNN features from both CT and X-ray images. Our results suggest that the proposed GMM-CNN features could improve the prediction of COVID-19 in chest CT and X-ray scans.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
COVID-19
Type of study:
Diagnostic study
/
Prognostic study
/
Randomized controlled trials
Limits:
Humans
Language:
English
Journal:
IEEE Trans Neural Netw Learn Syst
Year:
2022
Document Type:
Article
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