Journal of Artificial Intelligence, Machine Learning and Soft Computing

Abstract

Science and technology have improved our quality of life, but some industries' rapid development has given up people's future living environment and harms survival. Chest X-Ray (CXR) plays an essential role in the detection. Yet, the less availability of expert radiologists to interpret the CXR images and the subtle appearance of disease radiographic responses remains the major issue in manual diagnosis. Manual diagnosis is very complex and time-consuming. Automatic COVID (coronavirus) screening (ACoS) system uses radiomics texture descriptors extracted from CXR images to detect the normal, suspected, and nCOVID-19 infected patients. But this system is also time-consuming. Hence we propose a System for COVID-19 detection. The diagnosis of COVID-19 is typically associated with both the symptoms of pneumonia and Chest X-ray tests. CXR is the first imaging method that plays a vital role in the diagnosis of COVID-19 disease. In the existing system, we find some disadvantages; to overcome this, we will use X-ray data of normal and COVID 19 positive patients and train a model to differentiate between them. We present COVID 19 AI Detector using a deep convolutional neural network model (CNN) to triage patients for appropriate testing.

Keywords: - CNN, Machine Learning, Classification Algorithm, Covid-19 Detection.