An automated COVID analysis using Deep Learning
Author(s):
Abrar Rashid, Yogesh
Keywords:
Covid-19, X-Ray, CT scan. Deep Learning, Convolution neural network.
Abstract
Advancements in technology has put a rapid and huge impact on every field of life, be it defence, commerce, medical or any other field. Artificial intelligence has exhibited affirmative results in health care industry through its decision-making capability by analysing the data. More than 100 countries got affected by COVID-19 during a matter of no time. In the coming future, people are highly vulnerable to its incoming consequences. It is crucial to find out and develop a control system which will help in detecting the corona virus. To control the current mayhem, one of the solutions can be to use various AI tools in the diagnosis of this disease. According to a clinical study of COVID-19 infected patients, it was depicted that these patients, after coming in contact with this virus, are highly infected from a lung infection. The more efficient imaging techniques for detecting lungs related problems are chest x-ray (i.e., radiography) and chest CT. A chest x-ray is considerably a lower cost process in comparison to a chest CT. One of the most efficacious technique of machine learning is considered to be deep learning. It provides numerous beneficial analysis to study a very large number of images of chest x-rays which can have crucial effect on screening of Covid-19. In this work, we have taken into consideration the chest x-ray and CT scans of healthy patients as well as covid-19 affected patients and then trained our model to produce the results for the detection of virus.
Article Details
Unique Paper ID: 156544

Publication Volume & Issue: Volume 9, Issue 3

Page(s): 850 - 855
Article Preview & Download


Share This Article

Conference Alert

NCSST-2023

AICTE Sponsored National Conference on Smart Systems and Technologies

Last Date: 25th November 2023

SWEC- Management

LATEST INNOVATION’S AND FUTURE TRENDS IN MANAGEMENT

Last Date: 7th November 2023

Go To Issue



Call For Paper

Volume 10 Issue 1

Last Date for paper submitting for March Issue is 25 June 2023

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews