Covid-19 Prevalence Prediction Using Resnet-50 Deep Learning Technique.

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Bibliographic Details
Title:	Covid-19 Prevalence Prediction Using Resnet-50 Deep Learning Technique.
Authors:	Vidhya, K.¹ vidhya.k@kpriet.ac.in, B., Nagarajan², Kalaiselvi, R.³
Source:	Turkish Online Journal of Qualitative Inquiry. 2021, Vol. 12 Issue 3, p1-9. 9p.
Subject Terms:	COVID-19, X-ray imaging, Computed tomography, Data augmentation, COVID-19 testing, Deep learning
Abstract:	Covid 19 is the life threatening disease which spreads widely and rapidly. It mainly affects the breathing organs of our body as a first feat and then it causes various related infections gradually. Right from the day to day activity of human to economic growth of the whole world are totally affected by this deadly virus. Covid-19 could be a communal transmissible disease, and one nation could prepare a COVID-19 antibody alert. Clinical assumptions about COVID-19 diseased patients have exposed that these types of patients are largely adulterated with lung disease after exposure to the disease. So early prediction of COVID 19 related infection is the need for the day. Chest x-ray (i.e., radiography) and chest CT is the utmost operative imaging modalities for spotting complications. Nevertheless, a big chest x-ray may be a lesser significance likened to a CT scan. In-depth interpretation is the most prolific process of machine learning, given that valued research to consider the large number of chest x-ray images that can have a substantial influence on Covid-19 testing. The proposed work examines the PA form of chest X-ray images of Covid 19 affected patients and healthy patients. Once after collection of data through augmentation, the classification is performed by ResNet 50 model with the Deep Belief Network (DBN). By the way the system identifies the patients affected by COVID 19.In this analysis, chest X-ray samples were collected from the COVID -Xray-5k database. [ABSTRACT FROM AUTHOR]
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Database:	Education Research Complete

Description
Abstract:	Covid 19 is the life threatening disease which spreads widely and rapidly. It mainly affects the breathing organs of our body as a first feat and then it causes various related infections gradually. Right from the day to day activity of human to economic growth of the whole world are totally affected by this deadly virus. Covid-19 could be a communal transmissible disease, and one nation could prepare a COVID-19 antibody alert. Clinical assumptions about COVID-19 diseased patients have exposed that these types of patients are largely adulterated with lung disease after exposure to the disease. So early prediction of COVID 19 related infection is the need for the day. Chest x-ray (i.e., radiography) and chest CT is the utmost operative imaging modalities for spotting complications. Nevertheless, a big chest x-ray may be a lesser significance likened to a CT scan. In-depth interpretation is the most prolific process of machine learning, given that valued research to consider the large number of chest x-ray images that can have a substantial influence on Covid-19 testing. The proposed work examines the PA form of chest X-ray images of Covid 19 affected patients and healthy patients. Once after collection of data through augmentation, the classification is performed by ResNet 50 model with the Deep Belief Network (DBN). By the way the system identifies the patients affected by COVID 19.In this analysis, chest X-ray samples were collected from the COVID -Xray-5k database. [ABSTRACT FROM AUTHOR]
ISSN:	13096591