COVID-19 Detection using Deep Learning

Roy, Shovan (2020) COVID-19 Detection using Deep Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

Since the emergence of COVID-19, the planet has been plunged into a state of complete turmoil. The World Health Organisation (WHO) has proclaimed the virus to be a pandemic, with each nation taking measures in conjunction with the epidemic by implementing a lock-down to deter the spread of COVID-19 and offering immediate medical services to persons with symptoms or positive results. Owing to the size of the population affected and undergoing monitoring, the small amount of facilities and services accessible to hospitals is not adequate. The world is still lagging behind in terms of the time needed for the arrival of the test results, which not only take hours, but often days. This may be quite dangerous, because the suspected patients will have transmitted the infection to other individuals during that period, because they do not realize they are infected and would be self-isolating. Our motivation is to use deep learning Algorithms to be able to train a model which will detect COVID patients faster and more accurately than the RT-PCR being used today. CNN is used on X-ray scans for image processing and pattern detection to be able to detect COVID X-rays. The accuracy of 96% was achieved during the research which could be very helpful in these dire times.
Keywords: COVID-19, Classification, COVID-19 Detection, Convolutional Neural Network, CNN, Deep Learning, Image processing, Pattern Recognition, Chest X-rays

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software R Medicine > Healthcare Industry
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Dan English
Date Deposited:	20 Jan 2021 18:25
Last Modified:	20 Jan 2021 18:25
URI:	https://norma.ncirl.ie/id/eprint/4414

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