Open AccessDeep Learning Technique for Covid-19 Detection
Author(s) -
Khelili Mohamed Akram,
Sihem Slatnia,
Okba Kazar
Publication year - 2021
Publication title -
the eurasia proceedings of health, environment and life sciences
Language(s) - English
Resource type - Journals
ISSN - 2791-8033
DOI - 10.55549/ephels.3
Subject(s) - covid-19 , pandemic , deep learning , quarantine , pneumonia , public health , artificial intelligence , computer science , disease , medicine , virology , intensive care medicine , pathology , infectious disease (medical specialty) , outbreak
Nowadays, the detection of coronavirus disease 2019 (COVID-19) is one of the main challenges in the world, due to the rapid spreading of this viral disease. Currently, new variant of covid-19 virus was discovered in south Africa, India, and United Kingdom (UK) due to the mutation of the virus. Owing this critical situation of the world health and with increased number of the cases with the absence of efficient a cure vaccine, early and accurate detection of COVID-19 is necessity of time to prevent and control this pandemic by timely quarantine and medical treatment. Chest x-ray is the most suitable imaging technique for diagnosing in term of effectiveness and cost. Deep learning techniques have achieved state-of-the-art performances in computer-aided medical diagnosis which provides useful analysis to study a large amount of chest x-ray images that can critically impact on detecting the presence of Covid-19. In this work we present Deep Learning-based techniques for detecting Covid-19 and well differentiate between Covid-19 and Pneumonia disease using public dataset of 6432 X-ray images. The proposed model achieves 93% of accuracy, 95% of precision, 97% of recall, and 95% For f1-score.