An Adaptive Data Processing Framework for Cost-Effective COVID-19 and Pneumonia Detection
7th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2021
; 2021.
Article
in English
| Scopus | ID: covidwho-1769637
ABSTRACT
Medical imaging modalities have been showing great potentials for faster and efficient disease transmission control and containment. In the paper, we propose a cost-effective COVID-19 and pneumonia detection framework using CT scans acquired from several hospitals. To this end, we incorporate a novel data processing framework that utilizes 3D and 2D CT scans to diversify the trainable inputs in a resource-limited setting. Moreover, we empirically demonstrate the significance of several data processing schemes for our COVID-19 and pneumonia detection network. Experiment results show that our proposed pneumonia detection network is comparable to other pneumonia detection tasks integrated with imaging modalities, with 93% mean AUC and 85.22% mean accuracy scores on generalized datasets. Additionally, our proposed data processing framework can be easily adapted to other applications of CT modality, especially for cost-effective and resource-limited scenarios, such as breast cancer detection, pulmonary nodules diagnosis, etc. © 2021 IEEE
chest computerized tomography; COVID-19 screening; data processing; pneumonia detection; Cost effectiveness; Data handling; Diagnosis; Disease control; Medical imaging; Cost effective; CT-scan; Detection framework; Detection networks; Disease transmission; Imaging modality; Transmission control; Computerized tomography
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
Language:
English
Journal:
7th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2021
Year:
2021
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS