Detection of COVID-19 in Chest X-ray Images: A Big Data Enabled Deep Learning Approach.
Int J Environ Res Public Health
; 18(19)2021 Sep 27.
Article
in English
| MEDLINE | ID: covidwho-1438624
ABSTRACT
Coronavirus disease (COVID-19) spreads from one person to another rapidly. A recently discovered coronavirus causes it. COVID-19 has proven to be challenging to detect and cure at an early stage all over the world. Patients showing symptoms of COVID-19 are resulting in hospitals becoming overcrowded, which is becoming a significant challenge. Deep learning's contribution to big data medical research has been enormously beneficial, offering new avenues and possibilities for illness diagnosis techniques. To counteract the COVID-19 outbreak, researchers must create a classifier distinguishing between positive and negative corona-positive X-ray pictures. In this paper, the Apache Spark system has been utilized as an extensive data framework and applied a Deep Transfer Learning (DTL) method using Convolutional Neural Network (CNN) three architectures -InceptionV3, ResNet50, and VGG19-on COVID-19 chest X-ray images. The three models are evaluated in two classes, COVID-19 and normal X-ray images, with 100 percent accuracy. But in COVID/Normal/pneumonia, detection accuracy was 97 percent for the inceptionV3 model, 98.55 percent for the ResNet50 Model, and 98.55 percent for the VGG19 model, respectively.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Deep Learning
/
COVID-19
Type of study:
Experimental Studies
Limits:
Humans
Language:
English
Year:
2021
Document Type:
Article
Affiliation country:
Ijerph181910147
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