Coronavirus Classification using Deep Convolutional Neural Network, Models. and Chest ,X-ray images

Abstract

The COVID-2019 virus, which was discovered for the first time in December 2019 in the city of Wuhan, China, went on to become a pandemic after rapidly spreading around the globe. As there are currently no reliable automated toolkits on the market, there has been an increase in the demand for supplementary diagnostic tools for COVID19 patients. It may be possible to improve the accuracy of the diagnosis of covid19 disease by making use of more recent developments in artificial intelligence (AI) approaches and radiological imaging. In this research, three different convolution neural networks were applied to raw chest x-rays before the histogram filter was used for the basic pre-processing. The goal was to automatically detect COVID-19. The results that we obtained using the three suggested models indicate that the ResNet50 model provides the greatest classification performance with 96% accuracy , while the InceptionV3 model only achieves 95% accuracy, and the Inception-ResNetV2 model only achieves 82% accuracy.

Keywords

• coronavirus
• cnn
• artificial intelligence
• deep learning