Covid -19 Detection from X-Rays with Aid of Convolutional Neural Network

The prediction of the COVID-19 patients at an early stage is an open challenge in the research community. CT is the tool used to diagnose lung diseases, such as pneumonia. However, the CT scan procedure has the fastest turnaround time when compared with the molecular diagnostic test achieved in the ordinary laboratory that offers comprehensive information relevant to pathology such that it offers quantitative measurement in the dimension of lung lesion. The faster-increasing number of death and confirmed cases causes overburden in various medical systems worldwide. Recently, ReverseTranscription Polymerase Chain Reaction (RT-PCR) test is mainly used for detecting COVID-19 disease. However, the RT-PCR test consumes more time usually days for exploring the result of patients. Screen large number of cases for treatment and successful isolation is a high priority for controlling the COVID-19 spread in the entire world. Due to the inconvenience caused by CT scans, Chest X-rays are now emerging for automotive detection. Conventional neural network (CNN) is favored for classifying COVID-19 patients using chest CT images]. The three-dimensional (3D) CNN is used for classifying pulmonary blood vessel veins using CT images. Deep CNN is used to categorize interstitial lung disease with CT images. However, it achieves better accuracy in the classification of a lung nodule. For real-time detection, a web application using python is developed in order to reduce the burden on health care practitioners. In addition to accurate and effective predictions, it also prevents radiologists from getting infected.