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.
【저자키워드】 COVID-19, public health, Corona virus, deep learning, Pneumonia, machine learning, big data, CNN, transfer learning, chest X-ray, data bricks, Apache Spark, ResNet50, InceptionV3, VGG19, SparkDL, 【초록키워드】 coronavirus disease, coronavirus, hospital, Diagnosis, learning, Spread, X-ray, COVID-19 outbreak, Accuracy, Research, Convolutional neural network, Model, chest X-ray, network, Apache Spark, disease, early stage, Chest X-ray images, Classifier, symptoms of COVID-19, positive, researcher, deep, resulting, detect, evaluated, applied, cause, Neural, DTL, 【제목키워드】 detection, learning, X-ray, chest X-ray, deep,