stdClass Object
(
    [id] => 13124
    [paper_index] => 202405-01-017041
    [title] => DECODING COVID-19: HARNESSING CNN MODELS FOR CHEST X-RAY CLASSIFICATION 
    [description] => 
    [author] => Prekshith C R, Dr. K. Vijayalakshmi
    [googlescholar] => 
    [doi] => https://doi.org/10.36713/epra17041
    [year] => 2024
    [month] => May
    [volume] => 10
    [issue] => 5
    [file] => 1220am_86.EPRA JOURNALS 17041.pdf
    [abstract] =>  COVID-19 is a new virus that infects the respiratory tract of the upper respiratory system and organs. Based on the worldwide epidemic, the number of illnesses and deaths was growing every day. Chest X-ray (CXR) pictures are beneficial for monitoring lung diseases, especially COVID-19. Deep learning (DL) is a popular computing concept that has been widely used in medical applications. Efforts to automatically diagnose COVID-19 have been beneficial. This study used convolution neural networks (CNN) models to develop a DL technology for binary classification of COVID-19 using CXR pictures. By reducing the number of layers and tweaking parameters, training time was reduced. The suggested model for training loss of 0.0444 and accuracy of 98.53%. In validation it demonstrates even higher proficiency attaining a loss of 0.0181 and accuracy of 99.17%. These findings highlight the need of using deep learning (DL) for early COVID-19 diagnosis and screening.
    [keywords] => CNN, COVID-19, X-ray, Model, Deep convolutional neural networks.
    [doj] => 2024-05-24
    [hit] => 717
    [status] => y
    [award_status] => P
    [orderr] => 86
    [journal_id] => 1
    [googlesearch_link] => 
    [edit_on] => 
    [is_status] => 1
    [journalname] => EPRA International Journal of Multidisciplinary Research (IJMR)
    [short_code] => IJMR
    [eissn] =>  2455-3662 (Online)
    [pissn] => - --
    [home_page_wrapper] => images/products_image/11.IJMR.png
)
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