Multi-Joint Unit Network for COVID-19 Diagnosis using X-Ray and CT Images
ACM International Conference Proceeding Series
; : 12-21, 2022.
Article
in English
| Scopus | ID: covidwho-20242817
ABSTRACT
The global COVID-19 pandemic has caused a health crisis globally. Automated diagnostic methods can control the spread of the pandemic, as well as assists physicians to tackle high workload conditions through the quick treatment of affected patients. Owing to the scarcity of medical images and from different resources, the present image heterogeneity has raised challenges for achieving effective approaches to network training and effectively learning robust features. We propose a multi-joint unit network for the diagnosis of COVID-19 using the joint unit module, which leverages the receptive fields from multiple resolutions for learning rich representations. Existing approaches usually employ a large number of layers to learn the features, which consequently requires more computational power and increases the network complexity. To compensate, our joint unit module extracts low-, same-, and high-resolution feature maps simultaneously using different phases. Later, these learned feature maps are fused and utilized for classification layers. We observed that our model helps to learn sufficient information for classification without a performance loss and with faster convergence. We used three public benchmark datasets to demonstrate the performance of our network. Our proposed network consistently outperforms existing state-of-the-art approaches by demonstrating better accuracy, sensitivity, and specificity and F1-score across all datasets. © 2022 ACM.
Additional Key Words and Phrases: COVID-19; Chest X-Ray and CT Images; Classification; Computer Assisted Diagnosis; Deep Learning; Benchmarking; Classification (of information); Computer aided diagnosis; Computer aided instruction; Computerized tomography; Image classification; Medical imaging; Patient treatment; Additional key word and phrase: COVID-19; Chest X-ray image; CT Image; Feature map; Key words; Key-phrase; Learn+; Multi-joint; COVID-19
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Diagnostic study
/
Prognostic study
Language:
English
Journal:
ACM International Conference Proceeding Series
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS