VQC-COVID-NET: Vector Quantization Contrastive Learning for Covid-19 Image Base Classification
9th NAFOSTED Conference on Information and Computer Science, NICS 2022
; : 247-251, 2022.
Article
in English
| Scopus | ID: covidwho-2236387
ABSTRACT
Today, the COVID-19 epidemic has become extremely widespread. The first step in combating COVID-19 is identifying cases of infection. Real-time reverse transcriptase polymerase chain reaction is the most common method for identifying COVID (RT-PCR). This method, however, has been compromised by a time-consuming, laborious, and complex manual process. In addition to the RT-PCR test, screening computed tomography scan (CT) or X-ray images may be used to identify positive COVID-19 results, which could aid in the detection of COVID-19. Because of the continuing increase in new infections, the development of automated techniques for COVID-19 detection utilizing CT images is in high demand. This will aid in clinical diagnosis and alleviate the arduous task of image interpretation. Aggregating instances from various medical systems is highly advantageous for enlarging datasets for the development of machine learning techniques and the acquisition of robust, generalizable models. This study proposes a novel method for addressing distinct feature normalization in latent space due to cross-site domain shift in order to accurately execute COVID-19 identification using heterogeneous datasets with distribution disagreement. We propose using vector quantization to enhance the domain invariance of semantic embeddings in order to enhance classification performance on each dataset. We use two large, publicly accessible COVID-19 diagnostic CT scan datasets to develop and validate our proposed model. The experimental results demonstrate that our proposed method routinely outperforms state-of-the-art techniques on testing datasets. Public access to the implementation of our proposed method is available at https//github.com/khaclinh/VQC-COVID-NET. © 2022 IEEE.
COVID; domain invariance; entropy; representation learning; vector quantization; Classification (of information); Computerized tomography; Diagnosis; Image classification; Large dataset; Learning systems; Polymerase chain reaction; Semantics; Computed tomography scan; Images basis; Manual process; Real- time; Reverse transcriptase- polymerase chain reaction; Vector quantisation; X-ray image; COVID-19
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Diagnostic study
/
Prognostic study
/
Randomized controlled trials
Language:
English
Journal:
9th NAFOSTED Conference on Information and Computer Science, NICS 2022
Year:
2022
Document Type:
Article
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