Covid-19 Detection Using Chest X-Ray
Author(s):
Ahila T, Dr A C Subhajini
Keywords:
COVID-19, Coronavirus infections, Deep learning, Pneumonia, X-ray.
Abstract
COVID-19 continues to have a devastating impact on the lives of people all around the world. It is vital to screen the affected patients in a timely and cost-effective manner in order to combat this disease. Radiological examination is one of the most plausible steps in achieving this goal, with chest X-Ray being the most readily available and least priced option. We present a Deep Convolutional Neural Network-based approach to detect COVID-19 +ve patients using chest X-Ray pictures in this research. The implementation of a semi-quantitative CXR assessment has resulted from the addition of useful assistance to clinicians and the stratification of disease risk. Both severity scores and CXR results diagnosed early stage COVID-19 disease in this study. CXRs abnormalities were detected in 278 of 350 patients (78%) at certain points of the disease course. The DarkNet model was used in our study as a classifier for the you only look once (YOLO) real time object detection system. We implemented 17 convolutional layers and introduced different filtering on each layer. We have created a graphical user interface (GUI) application for public use. This application can be used by any medical personnel on any computer to detect COVID +ve patients using Chest X-Ray images in a matter of seconds.
Article Details
Unique Paper ID: 155791

Publication Volume & Issue: Volume 9, Issue 1

Page(s): 1788 - 1792
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