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CovXmlc: High performance COVID-19 detection on X-ray images using Multi-Model classification.
Verma, Sourabh Singh; Prasad, Ajay; Kumar, Anil.
  • Verma SS; SCIT, Manipal University Jaipur, India.
  • Prasad A; SCS, University of Petroleum and Energy Studies, Dehradun, India.
  • Kumar A; CSE, DIT, Dehradun, India.
Biomed Signal Process Control ; 71: 103272, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1525711
ABSTRACT
The Coronavirus Disease 2019 (COVID-19) outbreak has a devastating impact on health and the economy globally, that's why it is critical to diagnose positive cases rapidly. Currently, the most effective test to detect COVID-19 is Reverse Transcription-polymerase chain reaction (RT-PCR) which is time-consuming, expensive and sometimes not accurate. It is found in many studies that, radiology seems promising by extracting features from X-rays. COVID-19 motivates the researchers to undergo the deep learning process to detect the COVID- 19 patient rapidly. This paper has classified the X-rays images into COVID- 19 and normal by using multi-model classification process. This multi-model classification incorporates Support Vector Machine (SVM) in the last layer of VGG16 Convolution network. For synchronization among VGG16 and SVM we have added one more layer of convolution, pool, and dense between VGG16 and SVM. Further, for transformations and discovering the best result, we have used the Radial Basis function. CovXmlc is compared with five existing models using different parameters and metrics. The result shows that our proposed CovXmlc with minimal dataset reached accuracy up to 95% which is significantly higher than the existing ones. Similarly, it also performs better on other metrics such as recall, precision and f-score.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Biomed Signal Process Control Year: 2022 Document Type: Article Affiliation country: J.bspc.2021.103272

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Biomed Signal Process Control Year: 2022 Document Type: Article Affiliation country: J.bspc.2021.103272