Coronavirus disease (novel COVID-19) detection in Chest X-Ray images using CNN model

  Shweta Achyut Adurkar,  Siddhi Satish Kate,  Mayuri Sunil Solankure,  Ankita Ananda Gend,  Utkarsha Rajvardhan Desai

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 andtime-consuming. Automatic COVID (coronavirus) screening (ACoS) system uses radiomics texture descriptors extracted from CXR images to detect the normal, suspected, and COVID-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- Deep learning, Covid-19, CNN, Algorithm

Unique Identification Number - IJEDR2103015
Page Number(s) - 97-104
Pubished in - Volume 9 | Issue 3 | August 2021
DOI (Digital Object Identifier) -    http://doi.one/10.1729/Journal.28066
Publisher - IJEDR (ISSN - 2321-9939)

  Shweta Achyut Adurkar,  Siddhi Satish Kate,  Mayuri Sunil Solankure,  Ankita Ananda Gend,  Utkarsha Rajvardhan Desai,   "Coronavirus disease (novel COVID-19) detection in Chest X-Ray images using CNN model", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.9, Issue 3, pp.97-104, August 2021, Available at :http://www.ijedr.org/papers/IJEDR2103015.pdf