Computer-Aided Diagnosis of COVID-19 CT Scans Based on Spatiotemporal Information Fusion
Coronavirus disease (COVID-19) is highly contagious and pathogenic. Currently, the diagnosis of COVID-19 is based on nucleic acid testing, but it has false negatives and hysteresis. The use of lung CT scans can help screen and effectively monitor diagnosed cases. The application of computer-aided di...
Main Authors: | , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2021-01-01
|
Series: | Journal of Healthcare Engineering |
Online Access: | http://dx.doi.org/10.1155/2021/6649591 |
id |
doaj-73f8382714fc40f598722a7798847153 |
---|---|
record_format |
Article |
spelling |
doaj-73f8382714fc40f598722a77988471532021-03-15T00:01:08ZengHindawi LimitedJournal of Healthcare Engineering2040-23092021-01-01202110.1155/2021/6649591Computer-Aided Diagnosis of COVID-19 CT Scans Based on Spatiotemporal Information FusionTianyi Li0Wei Wei1Lidan Cheng2Shengjie Zhao3Chuanjun Xu4Xia Zhang5Yi Zeng6Jihua Gu7College of Optoelectronic Science and EngineeringCollege of Optoelectronic Science and EngineeringCollege of Optoelectronic Science and EngineeringMeBotX Intelligent Technology (Suzhou) Co. Ltd.The Department of RadiologyThe Department of TuberculosisThe Department of TuberculosisCollege of Optoelectronic Science and EngineeringCoronavirus disease (COVID-19) is highly contagious and pathogenic. Currently, the diagnosis of COVID-19 is based on nucleic acid testing, but it has false negatives and hysteresis. The use of lung CT scans can help screen and effectively monitor diagnosed cases. The application of computer-aided diagnosis technology can reduce the burden on doctors, which is conducive to rapid and large-scale diagnostic screening. In this paper, we proposed an automatic detection method for COVID-19 based on spatiotemporal information fusion. Using the segmentation network in the deep learning method to segment the lung area and the lesion area, the spatiotemporal information features of multiple CT scans are extracted to perform auxiliary diagnosis analysis. The performance of this method was verified on the collected dataset. We achieved the classification of COVID-19 CT scans and non-COVID-19 CT scans and analyzed the development of the patients’ condition through the CT scans. The average accuracy rate is 96.7%, sensitivity is 95.2%, and F1 score is 95.9%. Each scan takes about 30 seconds for detection.http://dx.doi.org/10.1155/2021/6649591 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tianyi Li Wei Wei Lidan Cheng Shengjie Zhao Chuanjun Xu Xia Zhang Yi Zeng Jihua Gu |
spellingShingle |
Tianyi Li Wei Wei Lidan Cheng Shengjie Zhao Chuanjun Xu Xia Zhang Yi Zeng Jihua Gu Computer-Aided Diagnosis of COVID-19 CT Scans Based on Spatiotemporal Information Fusion Journal of Healthcare Engineering |
author_facet |
Tianyi Li Wei Wei Lidan Cheng Shengjie Zhao Chuanjun Xu Xia Zhang Yi Zeng Jihua Gu |
author_sort |
Tianyi Li |
title |
Computer-Aided Diagnosis of COVID-19 CT Scans Based on Spatiotemporal Information Fusion |
title_short |
Computer-Aided Diagnosis of COVID-19 CT Scans Based on Spatiotemporal Information Fusion |
title_full |
Computer-Aided Diagnosis of COVID-19 CT Scans Based on Spatiotemporal Information Fusion |
title_fullStr |
Computer-Aided Diagnosis of COVID-19 CT Scans Based on Spatiotemporal Information Fusion |
title_full_unstemmed |
Computer-Aided Diagnosis of COVID-19 CT Scans Based on Spatiotemporal Information Fusion |
title_sort |
computer-aided diagnosis of covid-19 ct scans based on spatiotemporal information fusion |
publisher |
Hindawi Limited |
series |
Journal of Healthcare Engineering |
issn |
2040-2309 |
publishDate |
2021-01-01 |
description |
Coronavirus disease (COVID-19) is highly contagious and pathogenic. Currently, the diagnosis of COVID-19 is based on nucleic acid testing, but it has false negatives and hysteresis. The use of lung CT scans can help screen and effectively monitor diagnosed cases. The application of computer-aided diagnosis technology can reduce the burden on doctors, which is conducive to rapid and large-scale diagnostic screening. In this paper, we proposed an automatic detection method for COVID-19 based on spatiotemporal information fusion. Using the segmentation network in the deep learning method to segment the lung area and the lesion area, the spatiotemporal information features of multiple CT scans are extracted to perform auxiliary diagnosis analysis. The performance of this method was verified on the collected dataset. We achieved the classification of COVID-19 CT scans and non-COVID-19 CT scans and analyzed the development of the patients’ condition through the CT scans. The average accuracy rate is 96.7%, sensitivity is 95.2%, and F1 score is 95.9%. Each scan takes about 30 seconds for detection. |
url |
http://dx.doi.org/10.1155/2021/6649591 |
work_keys_str_mv |
AT tianyili computeraideddiagnosisofcovid19ctscansbasedonspatiotemporalinformationfusion AT weiwei computeraideddiagnosisofcovid19ctscansbasedonspatiotemporalinformationfusion AT lidancheng computeraideddiagnosisofcovid19ctscansbasedonspatiotemporalinformationfusion AT shengjiezhao computeraideddiagnosisofcovid19ctscansbasedonspatiotemporalinformationfusion AT chuanjunxu computeraideddiagnosisofcovid19ctscansbasedonspatiotemporalinformationfusion AT xiazhang computeraideddiagnosisofcovid19ctscansbasedonspatiotemporalinformationfusion AT yizeng computeraideddiagnosisofcovid19ctscansbasedonspatiotemporalinformationfusion AT jihuagu computeraideddiagnosisofcovid19ctscansbasedonspatiotemporalinformationfusion |
_version_ |
1714785333383528448 |