Selected article for: "convolutional neural network and diagnosis model"
Author: Shah, Vruddhi; Keniya, Rinkal; Shridharani, Akanksha; Punjabi, Manav; Shah, Jainam; Mehendale, Ninad
Title: Diagnosis of COVID-19 using CT scan images and deep learning techniques
  • Cord-id: d1efugtz
  • Document date: 2021_2_1
    • ID: d1efugtz
    Snippet: Early diagnosis of the coronavirus disease in 2019 (COVID-19) is essential for controlling this pandemic. COVID-19 has been spreading rapidly all over the world. There is no vaccine available for this virus yet. Fast and accurate COVID-19 screening is possible using computed tomography (CT) scan images. The deep learning techniques used in the proposed method is based on a convolutional neural network (CNN). Our manuscript focuses on differentiating the CT scan images of COVID-19 and non-COVID 1
    Document: Early diagnosis of the coronavirus disease in 2019 (COVID-19) is essential for controlling this pandemic. COVID-19 has been spreading rapidly all over the world. There is no vaccine available for this virus yet. Fast and accurate COVID-19 screening is possible using computed tomography (CT) scan images. The deep learning techniques used in the proposed method is based on a convolutional neural network (CNN). Our manuscript focuses on differentiating the CT scan images of COVID-19 and non-COVID 19 CT using different deep learning techniques. A self-developed model named CTnet-10 was designed for the COVID-19 diagnosis, having an accuracy of 82.1%. Also, other models that we tested are DenseNet-169, VGG-16, ResNet-50, InceptionV3, and VGG-19. The VGG-19 proved to be superior with an accuracy of 94.52% as compared to all other deep learning models. Automated diagnosis of COVID-19 from the CT scan pictures can be used by the doctors as a quick and efficient method for COVID-19 screening. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1007/s10140-020-01886-y)

    Search related documents:
    Co phrase search for related documents
    • accuracy achieve and acute respiratory disease: 1, 2
    • accuracy achieve and acute sars cov respiratory syndrome coronavirus: 1, 2, 3, 4, 5
    • accuracy achieve and loss function: 1, 2
    • accuracy achieve and low sensitivity: 1, 2, 3
    • accuracy give and acute sars cov respiratory syndrome coronavirus: 1
    • accuracy give and loss function: 1
    • accuracy improve and achieve accuracy: 1, 2, 3, 4, 5, 6, 7
    • accuracy improve and acute respiratory disease: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11
    • accuracy improve and acute sars cov respiratory syndrome coronavirus: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
    • accuracy improve and loss function: 1, 2, 3, 4
    • accuracy improve and low sensitivity: 1, 2, 3
    • accuracy rate and achieve accuracy: 1, 2, 3, 4, 5, 6
    • accuracy rate and acute respiratory disease: 1, 2
    • accuracy rate and acute sars cov respiratory syndrome coronavirus: 1, 2, 3, 4, 5, 6
    • accuracy rate and loss function: 1, 2
    • accuracy rate and low sensitivity: 1
    • accuracy result and achieve accuracy: 1, 2, 3
    • accuracy result and acute sars cov respiratory syndrome coronavirus: 1, 2, 3
    • achieve accuracy and low sensitivity: 1, 2, 3