Selected article for: "CNN model and deep learning"
Author: Hussain, Md Gulzar; Shiren, Ye
Title: Recognition of COVID-19 Disease Utilizing X-Ray Imaging of the Chest Using CNN
  • Cord-id: m27acxpx
  • Document date: 2021_9_5
    • ID: m27acxpx
    Snippet: Since this COVID-19 pandemic thrives, the utilization of X-Ray images of the Chest (CXR) as a complementary screening technique to RT-PCR testing grows to its clinical use for respiratory complaints. Many new deep learning approaches have developed as a consequence. The goal of this research is to assess the convolutional neural networks (CNNs) to diagnosis COVID-19 utisizing X-ray images of chest. The performance of CNN with one, three, and four convolution layers has been evaluated in this res
    Document: Since this COVID-19 pandemic thrives, the utilization of X-Ray images of the Chest (CXR) as a complementary screening technique to RT-PCR testing grows to its clinical use for respiratory complaints. Many new deep learning approaches have developed as a consequence. The goal of this research is to assess the convolutional neural networks (CNNs) to diagnosis COVID-19 utisizing X-ray images of chest. The performance of CNN with one, three, and four convolution layers has been evaluated in this research. A dataset of 13,808 CXR photographs are used in this research. When evaluated on X-ray images with three splits of the dataset, our preliminary experimental results show that the CNN model with three convolution layers can reliably detect with 96 percent accuracy (precision being 96 percent). This fact indicates the commitment of our suggested model for reliable screening of COVID-19.

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