Author: Xueyan Mei; Hao-Chih Lee; Kaiyue Diao; Mingqian Huang; Bin Lin; Chenyu Liu; Zongyu Xie; Yixuan Ma; Philip M. Robson; Michael Chung; Adam Bernheim; Venkatesh Mani; Claudia Calcagno; Kunwei Li; Shaolin Li; Hong Shan; Jian Lv; Tongtong Zhao; Junli Xia; Qihua Long; Sharon Steinberger; Adam Jacobi; Timothy Deyer; Marta Luksza; Fang Liu; Brent P. Little; Zahi A. Fayad; Yang Yang
Title: Artificial intelligence for rapid identification of the coronavirus disease 2019 (COVID-19) Document date: 2020_4_17
ID: 79tozwzq_57
Snippet: We used Inception-ResNet-v2 25 as the slice selection CNN to identify abnormal CT images from all chest CT images 26 . The slice selection CNN was pre-trained in a previous TB detection study on CT images from a total of 484 non-TB pneumonia patients, including bacterial pneumonia, viral pneumonia and fungal pneumonia, in addition to 439 pulmonary tuberculosis (PTB) and 155 normal chest CT patients. For CT images, the TB model predicts the probab.....
Document: We used Inception-ResNet-v2 25 as the slice selection CNN to identify abnormal CT images from all chest CT images 26 . The slice selection CNN was pre-trained in a previous TB detection study on CT images from a total of 484 non-TB pneumonia patients, including bacterial pneumonia, viral pneumonia and fungal pneumonia, in addition to 439 pulmonary tuberculosis (PTB) and 155 normal chest CT patients. For CT images, the TB model predicts the probabilities of 3 classes, including pulmonary tuberculosis (PTB), non-TB pneumonia and normal chest CT. This model achieved 99.4% accuracy in differentiating normal slices from abnormal (PTB and non-TB pneumonia) slices. In this work, we applied the TB model to a full CT scan to select 10 slices with the lowest probability of being normal. We noted that these selected slices may show no abnormal findings if the SARS-CoV-2 (+/-) patient's CT is normal.
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