Journal of Artificial Intelligence and Metaheuristics

Journal DOI

https://doi.org/10.54216/JAIM

Submit Your Paper

2833-5597ISSN (Online)

Volume 6 , Issue 1 , PP: 48-55, 2023 | Cite this article as | XML | Html | PDF | Full Length Article

CNN-Based Multiclass Classification for COVID-19 in Chest X-ray Images

S.K.Towfek 1 *

  • 1 Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA - (sktowfek@jcsis.org)
  • Doi: https://doi.org/10.54216/JAIM.060105

    Received: February 18, 2023 Revised: May 13, 2023 Accepted: November 17, 2023
    Abstract

    Managing the increasing number of patients requiring first screening can be significantly aided by real-time automated detection of COVID-19. It's feasible that Deep CNN models that have been trained on sufficiently large datasets will emerge as the most promising options for achieving the goal. This study aims to automatically detect and classify COVID-19 and viral pneumonia infections in chest X-ray images using a deep CNN model. Our proposed model relies on multiclass labeling to accomplish our aims. Design and Organization: Using the ImageNet pre-trained weights, the proposed model is built on top of the VGG16 framework. Additional custom layers were used to fine-tune the model and produce a better overall performance that is more specific to the goal. In terms of its subjects and methods, this study uses 15,153 samples in total. There are X-rays of the lungs from patients with COVID-19, those with viral pneumonia, and healthy volunteers. The entire dataset was split into an 80:20 split for the train and test sets before the model was trained. Image preprocessing and augmentation were used to enhance crucial parts of the photos before they were sent to the model in batches. We measure the model's efficacy with accuracy, precision, recall, and the F1 score. The analysis that was performed statistically was. The model's output is compared to the results of other recent research that have set the standard in the field. The proposed model has a 98% accuracy in multiclass classification on the test dataset, as measured by 98% precision, 96% recall, and 97% F1 score. Receiver operating characteristic curve area scores of 0.99 were achieved in all three multiclass classification situations. Finally, the proposed categorization model may show to be highly useful in the first diagnosis of COVID-19 and viral pneumonia patients, especially when dealing with heavy workloads and large volumes.

    Keywords :

    Keywork one , Keywork two , Keywork three , Keyword four , &hellip , .

    References

     

    [1]    Pneumonia. Available from: https://www.who.int/news-room/ fact-sheets/detail/pneumonia.

    [2]    Seladi-Schulman J. What to Know About COVID-19 and Pneumonia. Healthline.  Available  from:  https://www.healthline.com/health/ coronavirus-pneumonia#vs-regular-pneumonia.

    [3]    Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(22), 2402-2410, 2016.

    [4]    Gargeya R, Leng T, Automated identification of diabetic retinopathy using deep learning. Ophthalmology, 124(7), 962-969, 2017.

    [5]    Amrane M, Oukid S, Gagaoua I, Ensari T, Breast Cancer Classification Using Machine Learning. 2018 Electric Electronics, Computer Science, Biomedical Engineerings’ Meeting (EBBT), Istanbul, 1-4, 2018.

    [6]    Hussain L, Aziz W,  Saeed S,  Rathore S,  Rafique M. Automated Breast  Cancer  Detection  Using  Machine  Learning  Techniques  by Extracting  Different  Feature  Extracting  Strategies. 2018 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/12 IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE), 327-31, 2018.

    [7]    Xie Y,  Xia Y,  Zhang J,  Song Y,  Feng D,  Fulham M, et al,  Knowledge-based  collaborative  deep  learning  for  benign-malignant lung  nodule  classification  on  chest  CT.  IEEE  Trans  Med  Imaging, 38(4), 991-1004, 2019.

    [8]    Wu Q, Zhao W., Small-Cell Lung Cancer Detection Using a Supervised Machine Learning  Algorithm. 2017 International Symposium on Computer Science and Intelligent Controls (ISCSIC), Budapest, 88-91, 2017.

    [9]    Krizhevsky  A,  Sutskever I,  Hinton GE., Imagenet  Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems, 1097-105, 2012.

    [10]  Neuman MI, Lee EY, Bixby S, Diperna S, Hellinger J, Markowitz R, et al,  Variability  in  the  interpretation  of  chest  radiographs  for  the diagnosis of pneumonia in children. J Hosp Med, 7, 294-298, 2012.

    [11]  Chowdhury ME,  Rahman T,  Khandakar A,  Mazhar R,  Kadir MA, Mahbub  ZB,  et  al,  Can  AI  help  in  screening  viral  and  covid-19 pneumonia?,  IEEE Access, 8, 65-76, 2020.

    [12]  Rahman T, Khandakar A, Qiblawey Y, Tahir A, Kiranyaz S, Kashem SB, et al, Exploring the effect of image enhancement techniques on covid-19 detection using chest X-ray images. Computers in Biology and Medicine, 132, 2021.

    [13]  BIMCV-COVID19, Datasets Related to COVID19’s Pathology Course. Available from: https://bimcv.cipf.es/bimcv-projects/bimcv-covid19/#1 590858128006-9e640421-6711. [Last accessed on 2021 Mar 06].

    [14]  COVID-19-image-repository.    Available    from:    https://github.com/ ml-workgroup/covid-19-image-repository/tree/master/png.

    [15]   COVID-19    Database.   Available    from:    https://sirm.org/category/ senza-categoria/covid-19/.

    [16]  “Eurorad.org.” Eurorad. Available from: http://www.eurorad.org/. [Last accessed on 2021 Mar 06].

    [17]  “COVID-19 Image Data Collection.” Cohen2020covid, 2020. Available from: http://github.com/ieee8023/covid-chestxray-dataset.

    [18]  Arman H,  Mahdiyar MM,  Seokbum K.  COVID-19  Chest  X-Ray Image Repository. Figshare Dataset; 2020. Available from: https://doi. org/10.6084/m9.figshare. 12580328.

    [19]  “Armiro/COVID-CXNet.”  Armiro,   2020.  Available   from:   https:// github.com/armiro/COVID-CXNet.

    [20]  RSNA Pneumonia Detection Challenge. Available from: https://www. kaggle.com/c/rsna-pneumonia-detection-challenge/overview.  [Last accessed on 2021 Mar 06].

     

    Cite This Article As :
    , S.K.Towfek. CNN-Based Multiclass Classification for COVID-19 in Chest X-ray Images. Journal of Artificial Intelligence and Metaheuristics, vol. , no. , 2023, pp. 48-55. DOI: https://doi.org/10.54216/JAIM.060105
    , S. (2023). CNN-Based Multiclass Classification for COVID-19 in Chest X-ray Images. Journal of Artificial Intelligence and Metaheuristics, (), 48-55. DOI: https://doi.org/10.54216/JAIM.060105
    , S.K.Towfek. CNN-Based Multiclass Classification for COVID-19 in Chest X-ray Images. Journal of Artificial Intelligence and Metaheuristics , no. (2023): 48-55. DOI: https://doi.org/10.54216/JAIM.060105
    , S. (2023) . CNN-Based Multiclass Classification for COVID-19 in Chest X-ray Images. Journal of Artificial Intelligence and Metaheuristics , () , 48-55 . DOI: https://doi.org/10.54216/JAIM.060105
    S. [2023]. CNN-Based Multiclass Classification for COVID-19 in Chest X-ray Images. Journal of Artificial Intelligence and Metaheuristics. (): 48-55. DOI: https://doi.org/10.54216/JAIM.060105
    , S. "CNN-Based Multiclass Classification for COVID-19 in Chest X-ray Images," Journal of Artificial Intelligence and Metaheuristics, vol. , no. , pp. 48-55, 2023. DOI: https://doi.org/10.54216/JAIM.060105