COVID-19 Identification Using Deep Capsule Network: A Perspective of Super-Resolution CNN on Low-Qality CXR Images
7th International Conference on Communication and Information Processing, ICCIP 2021
; : 96-102, 2021.
Article
in English
| Scopus | ID: covidwho-1784903
ABSTRACT
Chest X-ray has become a useful method in the detection of coronavirus disease-19 (COVID-19). Due to the extreme global COVID-19 crisis, using the computerized diagnosis method for COVID-19 classification upon CXR images could significantly decrease clinician workload. We explicitly addressed the issue of low CXR image resolution by using Super-Resolution Convolutional Neural Network (SRCNN) to effectively reconstruct high-resolution (HR) CXR images from low-resolution (LR) CXR correspondents. Then, the HRCXR images are fed into the modified capsule network to retrieve distinct features for the classification of COVID-19. We demonstrate the proposed model on a public dataset and achieve ACC of 97.3%, SEN of 97.8%, SPE of 96.9%, and AUC of 98.0%. This new conceptual framework is proposed to play a vital task in the issue facing COVID-19 and related ailments. © 2021 ACM.
Capsule network; Chest x-ray; Convolutional Neural Network (CNN); COVID-19; Deep learning; Super-resolution; Computer aided diagnosis; Convolution; Convolutional neural networks; Image resolution; Chest x-rays; Convolutional neural network; Coronavirus disease-19; Coronaviruses; Diagnosis methods; High resolution; Superresolution; Coronavirus
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
7th International Conference on Communication and Information Processing, ICCIP 2021
Year:
2021
Document Type:
Article
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