Selected article for: "convolutional neural network and SARS status"
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_12
    Snippet: We first developed a deep convolutional neural network (CNN) to learn the imaging characteristics of SARS-CoV-2 on the initial CT scan. We then used support vector machine (SVM), random forest, and multi-layer perceptron (MLP) classifiers to classify the SARS-CoV-2 patients according to the clinical information. MLP showed the best performance on the tuning set; only MLP performance is reported hereafter. Finally, we created a neural network mode.....
    Document: We first developed a deep convolutional neural network (CNN) to learn the imaging characteristics of SARS-CoV-2 on the initial CT scan. We then used support vector machine (SVM), random forest, and multi-layer perceptron (MLP) classifiers to classify the SARS-CoV-2 patients according to the clinical information. MLP showed the best performance on the tuning set; only MLP performance is reported hereafter. Finally, we created a neural network model combining the radiologic data and the clinical information to predict SARS-CoV-2 status (Fig. 1) .

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