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
Published by: SCIENCE PRESS, BEIJING, CHINA
Distributed by:
China: All Local Post Offices
 
   
      1 Jan 2023, Volume 38 Issue 1   
    Article

    1. CLASSIFICATION OF COVID19 CT SCAN IMAGE USING CONVOLUTIONAL NEURAL NETWORK
    Akhilesh Kumar Shrivas1*, Amit Kumar Chandanan2, Amit Kumar Dewangan3, Vikas Kumar Pandey4, Vivek Kumar Sarathe5
    Journal of Data Acquisition and Processing, 2023, 38 (1): 1951-1962 . 

    Abstract

    Deep learning is a new machine learning technology that plays a significant role in different classification techniques. Image classification is a very important application of the deep neural network technique. We have used a deep learning technique in this research work to classify COVID19 CT scan images. The deep learning technique plays a vital role in developing a robust model for classifying the COVID19 CT Scan images. This research work proposed a special type of deep learning technique that is a convolutional neural network (CNN) to classify the COVID19 CT scan images. The main contribtion of this reserch work is to select the relevant tuning parameter of CNN which gives better accuracy for classifying COVID19 CT scan images. We analyzed the CNN model with different tuning parameters and compared the performance of the proposed CNN model with Relu, Sigmoid, and Tanh activation functions. The proposed CNN model achieved 88.24% accuracy under the condition of the Relu activation function.

    Keyword

    Classification, CT Scan Image, Convolutional Neural Network, Deep Learning, Activation function.


    PDF Download (click here)

SCImago Journal & Country Rank

ISSN 1004-9037

         

Home
Editorial Board
Author Guidelines
Subscription
Journal of Data Acquisition and Processing
Institute of Computing Technology, Chinese Academy of Sciences
P.O. Box 2704, Beijing 100190 P.R. China
E-mail: info@sjcjycl.cn
 
  Copyright ©2015 JCST, All Rights Reserved