Статья

DON: Deep Learning and Optimization-Based Framework for Detection of Novel Coronavirus Disease Using X-ray Images

G. Dhiman, K. Vinoth, A. Kaur, A. Sharma,
2021

In the hospital, a limited number of COVID-19 test kits are available due to the spike in cases every day. For this reason, a rapid alternative diagnostic option should be introduced as an automated detection method to prevent COVID-19 spreading among individuals. This article proposes multi-objective optimization and a deep-learning methodology for the detection of infected coronavirus patients with X-rays. J48 decision tree method classifies the deep characteristics of affected X-ray corona images to detect the contaminated patients effectively. Eleven different convolutional neuronal network-based (CNN) models were developed in this study to detect infected patients with coronavirus pneumonia using X-ray images (AlexNet, VGG16, VGG19, GoogleNet, ResNet18, ResNet500, ResNet101, InceptionV3, InceptionResNetV2, DenseNet201 and XceptionNet). In addition, the parameters of the CNN profound learning model are described using an emperor penguin optimizer with several objectives (MOEPO). A broad review reveals that the proposed model can categorise the X-ray images at the correct rates of precision, accuracy, recall, specificity and F1-score. Extensive test results show that the proposed model outperforms competitive models with well-known efficiency metrics. The proposed model is, therefore, useful for the real-time classification of X-ray chest images of COVID-19 disease. © 2021, International Association of Scientists in the Interdisciplinary Areas.

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Версии

  • 1. Version of Record от 2021-04-27

Метаданные

Об авторах
  • G. Dhiman
    Department of Computer Science, Government Bikram College of Commerce, Punjabi University, Patiala, Punjab 147001, India
  • K. Vinoth
    Department of Computer Science and Engineering, MVJ College of Engineering, Bangalore, India
  • A. Kaur
    Department of Computer Science, Sri Guru Granth Sahib World University, Fatehgarh Sahib, Punjab, India
  • A. Sharma
    Institute of Computer Technology and Information Security, Southern Federal University, Rostov-on-Don, Russian Federation
Название журнала
  • Interdisciplinary Sciences: Computational Life Sciences
Страницы
  • -
Издатель
  • Springer Science and Business Media Deutschland GmbH
Тип документа
  • journal article
Источник
  • scopus