Back to Search
Start Over
XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection Using Convolutional Neural Networks.
- Source :
- New Generation Computing; Nov2021, Vol. 39 Issue 3/4, p583-597, 15p
- Publication Year :
- 2021
-
Abstract
- COVID-19 (also known as SARS-COV-2) pandemic has spread in the entire world. It is a contagious disease that easily spreads from one person in direct contact to another, classified by experts in five categories: asymptomatic, mild, moderate, severe, and critical. Already more than 66 million people got infected worldwide with more than 22 million active patients as of 5 December 2020 and the rate is accelerating. More than 1.5 million patients (approximately 2.5% of total reported cases) across the world lost their life. In many places, the COVID-19 detection takes place through reverse transcription polymerase chain reaction (RT-PCR) tests which may take longer than 48 h. This is one major reason of its severity and rapid spread. We propose in this paper a two-phase X-ray image classification called XCOVNet for early COVID-19 detection using convolutional neural Networks model. XCOVNet detects COVID-19 infections in chest X-ray patient images in two phases. The first phase pre-processes a dataset of 392 chest X-ray images of which half are COVID-19 positive and half are negative. The second phase trains and tunes the neural network model to achieve a 98.44% accuracy in patient classification. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02883635
- Volume :
- 39
- Issue :
- 3/4
- Database :
- Complementary Index
- Journal :
- New Generation Computing
- Publication Type :
- Academic Journal
- Accession number :
- 153735040
- Full Text :
- https://doi.org/10.1007/s00354-021-00121-7