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10.1155/2020/8828855

http://scihub22266oqcxt.onion/10.1155/2020/8828855
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32849861!7439162!32849861
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suck abstract from ncbi


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pmid32849861      Int+J+Biomed+Imaging 2020 ; 2020 (ä): 8828855
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  • COVID-19 Deep Learning Prediction Model Using Publicly Available Radiologist-Adjudicated Chest X-Ray Images as Training Data: Preliminary Findings #MMPMID32849861
  • Che Azemin MZ; Hassan R; Mohd Tamrin MI; Md Ali MA
  • Int J Biomed Imaging 2020[]; 2020 (ä): 8828855 PMID32849861show ga
  • The key component in deep learning research is the availability of training data sets. With a limited number of publicly available COVID-19 chest X-ray images, the generalization and robustness of deep learning models to detect COVID-19 cases developed based on these images are questionable. We aimed to use thousands of readily available chest radiograph images with clinical findings associated with COVID-19 as a training data set, mutually exclusive from the images with confirmed COVID-19 cases, which will be used as the testing data set. We used a deep learning model based on the ResNet-101 convolutional neural network architecture, which was pretrained to recognize objects from a million of images and then retrained to detect abnormality in chest X-ray images. The performance of the model in terms of area under the receiver operating curve, sensitivity, specificity, and accuracy was 0.82, 77.3%, 71.8%, and 71.9%, respectively. The strength of this study lies in the use of labels that have a strong clinical association with COVID-19 cases and the use of mutually exclusive publicly available data for training, validation, and testing.
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