About: COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation
Creator	Eysenbach, Gunther Kim, Kyung Kim, Young Lee, Jae Kang, Seung Chung, Heewon Goodwin, Travis Jung, Hyunseok Kang, Wu Kim, Nan Ko, Hoon Lee, Jeongjin Lee, Jinseok Shin, Youngbin
source	Medline; PMC; WHO
abstract	BACKGROUND: Coronavirus disease (COVID-19) has spread explosively worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) is a relevant screening tool due to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely occupied fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. OBJECTIVE: We aimed to rapidly develop an AI technique to diagnose COVID-19 pneumonia in CT images and differentiate it from non–COVID-19 pneumonia and nonpneumonia diseases. METHODS: A simple 2D deep learning framework, named the fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning using one of four state-of-the-art pretrained deep learning models (VGG16, ResNet-50, Inception-v3, or Xception) as a backbone. For training and testing of FCONet, we collected 3993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and nonpneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training set and a testing set at a ratio of 8:2. For the testing data set, the diagnostic performance of the four pretrained FCONet models to diagnose COVID-19 pneumonia was compared. In addition, we tested the FCONet models on an external testing data set extracted from embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. RESULTS: Among the four pretrained models of FCONet, ResNet-50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100.00%, and accuracy 99.87%) and outperformed the other three pretrained models in the testing data set. In the additional external testing data set using low-quality CT images, the detection accuracy of the ResNet-50 model was the highest (96.97%), followed by Xception, Inception-v3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). CONCLUSIONS: FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing data set, the FCONet model based on ResNet-50 appears to be the best model, as it outperformed other FCONet models based on VGG16, Xception, and Inception-v3.
has issue date	2020-06-29(xsd:dateTime)
bibo:doi	10.2196/19569
bibo:pmid	32568730
has license	cc-by
schema:url	https://doi.org/10.2196/19569
resource representing a document's title	COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation
has PubMed Central identifier	PMC7332254
has PubMed identifier	32568730
schema:publication	J Med Internet Res
resource representing a document's body	covid:PMC7332254#body_text
is schema:about of	named entity 'triage' named entity 'COVID-19 testing' named entity 'ImageNet' named entity 'chest CT' named entity 'pneumonia' named entity 'quarantining' named entity 'COVID-19' named entity 'sensitivity, specificity' named entity 'ResNet' named entity 'nucleic acid' named entity 'testing data set' named entity 'AI models' named entity 'sensitivity and specificity' named entity 'lung' named entity 'ResNet' named entity 'sensitivity, specificity' named entity 'Chonnam National University' named entity 'pneumonia' named entity 'data augmentation' named entity 'PNG' named entity 'parenchymal' named entity 'Fleischner Society' named entity 'ResNet' named entity 'AI models' named entity 'infection' named entity 'DICOM' named entity 'COVID' named entity 'COVID-19' named entity 'COVID-19 pandemic' named entity 'COVID' named entity 'ResNet' named entity 'diagnostic aid' named entity 'chest CT' named entity 'data set' named entity 'receiver operating characteristic' named entity 'pneumonia' named entity 'TensorFlow' named entity 'Python' named entity 'deep learning' named entity 'COVID' named entity 'data set' named entity 'GeForce GTX 1080 Ti' named entity 'screening tool' named entity 'SD 17' named entity 'pneumonia' named entity 'API' named entity 'computed tomography (CT) scan' named entity 'ResNet' named entity 'false negative results' named entity 'pneumonia' named entity 'COVID' named entity 'COVID' named entity 'mental stress' named entity 'chest CT' named entity 'COVID' named entity 'probability' named entity 'heatmap' named entity 'heatmap' named entity 'COVID-19' named entity 'false positives' named entity 'COVID-19' named entity 'data set' named entity 'lung' named entity 'infection' named entity 'COVID' named entity 'image file format' named entity 'RT-PCR' named entity 'COVID-19' named entity 'ResNet'

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