Detection of COVID-19 in Chest X-ray Images: A Big Data Enabled Deep Learning Approach.
Mazhar Javed AwanMuhammad Haseeb BilalAwais YasinHaitham NobaneeNabeel Sabir KhanAzlan Mohd ZainPublished in: International journal of environmental research and public health (2021)
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.
Keyphrases
- coronavirus disease
- deep learning
- sars cov
- convolutional neural network
- big data
- artificial intelligence
- respiratory syndrome coronavirus
- machine learning
- early stage
- high resolution
- healthcare
- dual energy
- optical coherence tomography
- end stage renal disease
- chronic kidney disease
- radiation therapy
- computed tomography
- intensive care unit
- newly diagnosed
- mass spectrometry
- physical activity
- acute respiratory distress syndrome
- magnetic resonance
- loop mediated isothermal amplification
- mechanical ventilation