Identification of Coronavirus & Pneumonia from Chest x-ray Scans using Deep Multilayered CNN
2021 International Conference on Simulation, Automation and Smart Manufacturing, SASM 2021
; 2021.
Article
in English
| Scopus | ID: covidwho-2018980
ABSTRACT
Recently, COVID-19 disease carried out by the SARS-CoV-2 virus appeared as a pandemic across the world. The traditional diagnostic techniques are facing a hard time detecting the virus efficiently at an early stage. In this context, chest x-ray scans can be useful for diagnostic prediction. Therefore, in this paper, a deep multi-layered convolution neural network has been proposed to analyze the chest x-ray scans effectively for detecting COVID-19 and pneumonia accurately. The proposed approach has been applied on multiple benchmark datasets and the experimental results define the effectiveness of the proposed approach. © 2021 IEEE.
Chest X-ray scans; Coronavirus diagnosis; Deep Multilayered CNN; L2 Regularization; Convolutional neural networks; Diagnosis; Multilayer neural networks; Network layers; Chest X-ray scan; Chest x-rays; Convolution neural network; Coronavirus diagnose; Coronaviruses; Diagnostics techniques; Multi-layered; Regularisation; Coronavirus
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2021 International Conference on Simulation, Automation and Smart Manufacturing, SASM 2021
Year:
2021
Document Type:
Article
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