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Classification of COVID-19 and Pneumonia Using Deep Transfer Learning.
- Source :
- Journal of Healthcare Engineering; 12/16/2021, p1-11, 11p
- Publication Year :
- 2021
-
Abstract
- The World Health Organization (WHO) recognized COVID-19 as the cause of a global pandemic in 2019. COVID-19 is caused by SARS-CoV-2, which was identified in China in late December 2019 and is indeed referred to as the severe acute respiratory syndrome coronavirus-2. The whole globe was hit within several months. As millions of individuals around the world are infected with COVID-19, it has become a global health concern. The disease is usually contagious, and those who are infected can quickly pass it on to others with whom they come into contact. As a result, monitoring is an effective way to stop the virus from spreading further. Another disease caused by a virus similar to COVID-19 is pneumonia. The severity of pneumonia can range from minor to life-threatening. This is particularly hazardous for children, people over 65 years of age, and those with health problems or immune systems that are affected. In this paper, we have classified COVID-19 and pneumonia using deep transfer learning. Because there has been extensive research on this subject, the developed method concentrates on boosting precision and employs a transfer learning technique as well as a model that is custom-made. Different pretrained deep convolutional neural network (CNN) models were used to extract deep features. The classification accuracy was used to measure performance to a great extent. According to the findings of this study, deep transfer learning can detect COVID-19 and pneumonia from CXR images. Pretrained customized models such as MobileNetV2 had a 98% accuracy, InceptionV3 had a 96.92% accuracy, EffNet threshold had a 94.95% accuracy, and VGG19 had a 92.82% accuracy. MobileNetV2 has the best accuracy of all of these models. [ABSTRACT FROM AUTHOR]
- Subjects :
- COVID-19
SARS-CoV-2
DEEP learning
COVID-19 pandemic
CONVOLUTIONAL neural networks
Subjects
Details
- Language :
- English
- ISSN :
- 20402295
- Database :
- Complementary Index
- Journal :
- Journal of Healthcare Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 154174959
- Full Text :
- https://doi.org/10.1155/2021/3514821