A deep convolutional neural network architecture for interstitial lung disease pattern classification

2020 ◽  
Vol 58 (4) ◽  
pp. 725-737 ◽  
Author(s):  
Sheng Huang ◽  
Feifei Lee ◽  
Ran Miao ◽  
Qin Si ◽  
Chaowen Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Interstitial Lung Disease ◽  
Convolutional Neural Network ◽  
Lung Disease ◽  
Pattern Classification ◽  
Network Architecture ◽  
Deep Convolutional Neural Network ◽  
Neural Network Architecture ◽  
Disease Pattern

scholarly journals COVIDNet-CT: Detection of COVID-19 from Chest CT Images using a Tailored Deep Convolutional Neural Network Architecture

2021 ◽  
Vol 6 (1) ◽  
pp. 1-3
Author(s):  
Hayden Gunraj ◽  
Linda Wang ◽  
Alexander Wong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Screening Method ◽  
Ct Images ◽  
Chest Ct ◽  
Deep Convolutional Neural Network ◽  
Screening Tools ◽  
Neural Network Architecture ◽  
Tremendous Impact

The COVID-19 pandemic continues to have a tremendous impact on patients and healthcare systems around the world. To combat this disease, there is a need for effective screening tools to identify patients infected with COVID-19, and to this end CT imaging has been proposed as a key screening method to complement RT-PCR testing. Early studies have reported abnormalities in chest CT images which are characteristic of COVID-19 infection, but these abnormalities may be difficult to distinguish from abnormalities caused by other lung conditions. Motivated by this, we introduce COVIDNet-CT, a deep convolutional neural network architecture tailored for detection of COVID-19 cases from chest CT images. We also introduce COVIDx-CT, a CT image dataset comprising 104,009 images across 1,489 patient cases. Finally, we leverage explainability to investigate the decision-making behaviour of COVIDNet-CT and ensure that COVIDNet-CT makes predictions based on relevant indicators in CT images.

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