Multi-feature Multi-Scale CNN-Derived COVID-19 Classification from Lung Ultrasound Data.
Annu Int Conf IEEE Eng Med Biol Soc
; 2021: 2618-2621, 2021 11.
Article
in English
| MEDLINE | ID: covidwho-1566243
ABSTRACT
The global pandemic of the novel coronavirus disease 2019 (COVID-19) has put tremendous pressure on the medical system. Imaging plays a complementary role in the management of patients with COVID-19. Computed tomography (CT) and chest X-ray (CXR) are the two dominant screening tools. However, difficulty in eliminating the risk of disease transmission, radiation exposure and not being cost-effective are some of the challenges for CT and CXR imaging. This fact induces the implementation of lung ultrasound (LUS) for evaluating COVID-19 due to its practical advantages of noninvasiveness, repeatability, and sensitive bedside property. In this paper, we utilize a deep learning model to perform the classification of COVID-19 from LUS data, which could produce objective diagnostic information for clinicians. Specifically, all LUS images are processed to obtain their corresponding local phase filtered images and radial symmetry transformed images before fed into the multi-scale residual convolutional neural network (CNN). Secondly, image combination as the input of the network is used to explore rich and reliable features. Feature fusion strategy at different levels is adopted to investigate the relationship between the depth of feature aggregation and the classification accuracy. Our proposed method is evaluated on the point-of-care US (POCUS) dataset together with the Italian COVID-19 Lung US database (ICLUS-DB) and shows promising performance for COVID-19 prediction.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
COVID-19
Type of study:
Diagnostic study
/
Experimental Studies
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Annu Int Conf IEEE Eng Med Biol Soc
Year:
2021
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS