SarNet-1 -A Novel Architecture for Diagnosing Covid-19 Pneumonia and Pneumonia through Chest X-Ray Images

ABSTRACT

Coronavirus (COVID-19) is a contagious disease which begins with flu-like symptoms. COVID-19 arose in China and it rapidly spread throughout the globe, leading to a pandemic. For many, it was noticed that the infection started with fever, cough and finally leading to pneumonia. It is very necessary to differentiate between covid pneumonia and general pneumonia for appropriate treatment. Chest X-ray readings are useful for radiologists to identify the severity of infection. While computerising this mechanism, deep learning techniques are found to be very useful in extracting relevant features from medical images. This can help in differentiating pneumonia, COVID19 pneumonia and x-rays of a healthy person. Computer aided methods for identifying the presence of pneumonia can help health providers to a great extent for quick diagnosis. The X-ray's gathered from freely available datasets are used in this work to propose an architecture for categorising X-ray's into pneumonia and covid pneumonia. © 2022 International Journal on Recent and Innovation Trends in Computing and Communication. All rights reserved.