• Chandranayaka I R
• Sarikhaa K
• Shashi Gowda
• Rukmini B Rao

The novel coronavirus 2019 (COVID-2019) has become a pandemic disease, which first appeared in Wuhan city of China in December 2019. COVID-2019 has already caused thousands of causalities and infected several millions of people worldwide. It is critical to detect the positive cases as early as possible so as to prevent the further spread of this epidemic and to quickly treat affected patients. An infection caused by COVID-19, can be detected by a chest X-ray exam and should be treated appropriately. COVID-19 has emerged the need for computer-aided diagnosis with automatic, accurate, and fast algorithms. Since the symptoms of pneumonia and COVID-19 were almost similar, it become very important to distinguish the both, in order to provide proper diagnosis. The aim of the proposed project is to apply Machine Learning algorithm for COVID-19/ pneumonia detection over chest X-ray images. The database contains a mixture of COVID-19, viral pneumonia, and normal chest X-ray images. The proposed model uses different architectures of convolutional neural networks (CNNs) trained on ResNet, and adapt them to behave as feature extractors for the X-ray images. Proposed model can be very helpful to the front-line workers. It can also be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients.

Detection, COVID-19, chest X-ray, convolutional neural network

"Classification of pneumonia using chest x-ray images by applying AI and ML techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 7, page no.g95-g101, July-2022, Available :http://www.jetir.org/papers/JETIR2207614.pdf

"Classification of pneumonia using chest x-ray images by applying AI and ML techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 7, page no. ppg95-g101, July-2022, Available at : http://www.jetir.org/papers/JETIR2207614.pdf