Recognition of Covid-19 using Capsule Neural Networks on Chest X-rays

The COVID-19 pandemic, which began in China in December 2019, has had devastating effects on public health, daily life, and the global economy. It is crucial to identify positive cases quickly, to prevent the pandemic from spreading and to provide appropriate treatment for those affected. However, due to a lack of accurate automated tools, there is a growing demand for more diagnostic methods. Combining deep learning algorithms with radiological imaging can aid in the precise detection of COVID-19, particularly in rural areas where there is a shortage of qualified medical professionals. This study suggests a methodology for identifying COVID-19 from raw chest X-ray images using Capsule Neural Networks. The proposed model has achieved an accuracy of 93.58% and a loss rate of only 0.32, indicating better performance than existing convolution neural network models. This method could be valuable for healthcare professionals in the quick detection of COVID-19 cases.