Document Type : Primary Research paper
Assistant Professor, Professor Departmentof CSE PanimalarInstituteofTechnology
Assistant Professor Departmentof CSE PanimalarInstituteofTechnology
The coronavirus 2019 (COVID-19), which initially appeared in the Chinese city of Wuhan in December 2019, soon spread throughout the world and became an epidemic.It is critical to discover positive cases as soon as possible in order to prevent the illness from spreading further.The use of convolutional neural networks (CNN) methods in conjunction with medical imaging can aid in the accurate detection ofthis disease. A novel model for automatic COVID-19 identification utilising raw chest X-ray pictures is employed in this study.The model was created to provide reliable diagnosis for binary categorization (COVID vs No-Findings).Our model has a 98.44 percent accuracy rate.Keras and Tensorflow were used to train a model using the sequential model.On each lay, we used convolutional layers and applied various filtering.