Author
Listed:
- Rajeev Kumar Gupta
(Pandit Deendayal Energy University, Gandhinagar, India)
- Nilesh Kunhare
(Amity University, India)
- Rajesh Kumar Pateriya
(Maulana Azad National Institute Technology, India)
- Nikhlesh Pathik
(Sagar Institute of Science and Technology, India)
Abstract
The novel Covid-19 is one of the leading cause of death worldwide in the year 2020 and declared as a pandemic by world health organization (WHO). This virus affecting all countries across the world and 5 lakh people die as of June 2020 due to Covid-19. Due to the highly contagious nature, early detection of this virus plays a vital role to break Covid chain. Recent studies done by China says that chest CT and X-Ray image may be used as a preliminary test for Covid detection. Deep learning-based CNN model can use to detect Coronavirus automatically from the chest X-rays images. This paper proposed a transfer learning-based approach to detect Covid disease. Due to the less number of Covid chest images, we are using a pre-trained model to classify X-ray images into Covid and Normal class. This paper presents the comparative study of a various pre-trained model like VGGNet-19, ResNet50 and Inception_ResNet_V2. Experiment results show that Inception_ResNet_V2 gives the better result as compare to VGGNet and ResNet model with training and test accuracy of 99.26 and 94, respectively.
Suggested Citation
Rajeev Kumar Gupta & Nilesh Kunhare & Rajesh Kumar Pateriya & Nikhlesh Pathik, 2022.
"A Deep Neural Network for Detecting Coronavirus Disease Using Chest X-Ray Images,"
International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 17(2), pages 1-27, April.
Handle:
RePEc:igg:jhisi0:v:17:y:2022:i:2:p:1-27
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