Bimonthly    Since 1986
ISSN 1004-9037
Publication Details
Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Distributed by:
China: All Local Post Offices
      09 May 2023, Volume 38 Issue 3

    Dr.S.Vince Raicheal, Dr. M.V. Srinath, Dr.R.Jayakumar
    Journal of Data Acquisition and Processing, 2023, 38 (3): 786-801 . 


    Due to the COVID-19 pandemic, the whole world is experiencing a health catastrophe that is unprecedented in its scope. Since coronavirus spreads rapidly, investigators are worried about finding or assisting in the development of treatments to save lives and reduce the pandemic. The key issues in the present COVID-19 situation are initial identification and diagnosis of COVID-19, as well as precise parting of non-COVID-19 patients in cost-effective methods during the initial period of the illness. Artificial Intelligence (AI), for example, has been modified to solve the issues posed by pandemics. Deep Learning (DL) models' computation power has aided healthcare procedures to be more rapid, accurate, and well-organized. DL networks are revolutionising patient care, and they play a crucial role in clinical practise for health systems. DL approaches in healthcare include computer vision, Natural Language Processing (NLP), and fortification knowledge. DL is a strategy to tackle the COVID-19 outbreak because there are so many bases of medicinal pictures (e.g., X-ray, CT, and MRI). As a result of this statistic, numerous studies have been projected and produced for the first months of 2020. We create a DL method to extract features and identify COVID-19 from Radiology Modalities in this article. In spite of their widespread use in diagnostic centres, diagnostic procedures created on radiological examinations have flaws when it comes to the disease's uniqueness. As a result, DL models are commonly used to evaluate radiological pictures in order to identify the disease in the early stage. AI, notably DL, is being used to solve this challenge. A novel approach of entailing FJCovNet2 which is a DL method built on DenseNet121 to detect COVID-19 using CT-Scan and X-Ray pictures is effectuated. The comparative study of various forms of radiology modalities in deep learning determines the best accurate method to detect the disease earlier using the predefined models namely InceptionV3, ResNet 50, and VGG 16. The maximum accuracy of 98.23% is attained through the proposed model RJCovNet2.


    Internet of Things (IoT), Deep Learning (DL), Healthcare, COVID-19, CT scan, X-Ray

    PDF Download (click here)

SCImago Journal & Country Rank

ISSN 1004-9037


Editorial Board
Author Guidelines
Journal of Data Acquisition and Processing
Institute of Computing Technology, Chinese Academy of Sciences
P.O. Box 2704, Beijing 100190 P.R. China
  Copyright ©2015 JCST, All Rights Reserved