Application of deep learning to identify COVID-19 infection in posteroanterior chest X-rays
| Autor: | Qingqing Mao, Abigail Green-Saxena, Jacob Calvert, Andrea McCoy, Jana Hoffman, Ritankar Das, Jenish Maharjan, Emily Pellegrini |
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| Rok vydání: | 2021 |
| Předmět: |
Coronavirus disease 2019 (COVID-19)
Receiver operating characteristic Artificial neural network SARS-CoV-2 business.industry X-Rays Deep learning COVID-19 Pattern recognition Classification Convolutional neural network Set (abstract data type) Deep Learning Binary classification Artificial Intelligence Test set Diagnosis Humans Medicine Radiology Nuclear Medicine and imaging Neural Networks Computer Artificial intelligence business Neural networks |
| Zdroj: | Clinical Imaging |
| ISSN: | 0899-7071 |
| DOI: | 10.1016/j.clinimag.2021.07.004 |
| Popis: | Introduction Posteroanterior chest X-rays (CXRs) are recommended over computed tomography scans for COVID-19 diagnosis, as CXRs can be obtained with relatively low risk of facility contamination. The objective of this study was to assess seven configurations of six convolutional deep neural network architectures for classification of CXRs as COVID-19 positive or negative. Methods The primary dataset consisted of 294 COVID-19 positive and 294 COVID-19 negative CXRs, the latter comprising roughly equally many pneumonia, emphysema, fibrosis, and healthy images. We used six common convolutional neural network architectures, VGG16, DenseNet121, DenseNet201, MobileNet, NasNetMobile and InceptionV3. We studied six models (one for each architecture) which were pre-trained on a vast repository of generic (non-CXR) images, as well as a seventh, a DenseNet121 model which was pre-trained on a repository of CXR images. For each model, we replaced the output layers with custom fully connected layers for the task of binary classification of images as COVID-19 positive or negative. Performance metrics were calculated on a hold-out test set with CXRs from patients who were not included in the training/validation set. Results When pre-trained on generic images, the VGG16, DenseNet121, DenseNet201, MobileNet, NasNetMobile, and InceptionV3 architectures respectively produced hold-out test set areas under the receiver operating characteristic (AUROCs) of 0.98, 0.95, 0.97, 0.95, 0.99, and 0.96 for the COVID-19 classification of CXRs. The X-ray pre-trained DenseNet121 model, in comparison, had a test set AUROC of 0.87. Discussion Common convolutional neural network architectures with parameters pre-trained on generic images yield high-performance and well-calibrated COVID-19 CXR classification. |
| Databáze: | OpenAIRE |
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