Selected article for: "convolutional neural network and deep learning model"
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_46
    Snippet: The RT-PCR virology test (SARS-CoV-2 (+) or SARS-CoV-2 (-)) was used as the reference to train the models. We developed and evaluated three different models using CT images and clinical information. Firstly, a deep learning model using a convolutional neural network (Model 1) was developed to only use CT images to predict SARS-CoV-2 status. Secondly, conventional machine learning methods (Model 2), including support vector machine (SVM), random f.....
    Document: The RT-PCR virology test (SARS-CoV-2 (+) or SARS-CoV-2 (-)) was used as the reference to train the models. We developed and evaluated three different models using CT images and clinical information. Firstly, a deep learning model using a convolutional neural network (Model 1) was developed to only use CT images to predict SARS-CoV-2 status. Secondly, conventional machine learning methods (Model 2), including support vector machine (SVM), random forest and multilayer perceptron (MLP), were evaluated to predict SARS-CoV-2 using only clinical information.

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