Robust Automated Framework for COVID-19 Disease Identification from a Multicenter Dataset of Chest CT Scans

• URL: http://arxiv.org/abs/2109.09241v1
• Date: Sun, 19 Sep 2021 22:32:55 GMT
• Title: Robust Automated Framework for COVID-19 Disease Identification from a Multicenter Dataset of Chest CT Scans
• Authors: Shahin Heidarian, Parnian Afshar, Nastaran Enshaei, Farnoosh Naderkhani, Moezedin Javad Rafiee, Anastasia Oikonomou, Akbar Shafiee, Pascal N. Tyrrell, Faranak Babaki Fard, Konstantinos N. plataniotis, Arash Mohammadi
• Abstract summary: We show that our proposed model is trained on a relatively small dataset acquired from only one imaging center using a specific scanning protocol. We also showed that the model can be updated via an unsupervised approach to cope with the data shift between the train and test sets. Experimental results indicate that our proposed framework performs well on all test sets achieving total accuracy of 96.15%.
• Score: 27.29759500174996
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The objective of this study is to develop a robust deep learning-based framework to distinguish COVID-19, Community-Acquired Pneumonia (CAP), and Normal cases based on chest CT scans acquired in different imaging centers using various protocols, and radiation doses. We showed that while our proposed model is trained on a relatively small dataset acquired from only one imaging center using a specific scanning protocol, the model performs well on heterogeneous test sets obtained by multiple scanners using different technical parameters. We also showed that the model can be updated via an unsupervised approach to cope with the data shift between the train and test sets and enhance the robustness of the model upon receiving a new external dataset from a different center. We adopted an ensemble architecture to aggregate the predictions from multiple versions of the model. For initial training and development purposes, an in-house dataset of 171 COVID-19, 60 CAP, and 76 Normal cases was used, which contained volumetric CT scans acquired from one imaging center using a constant standard radiation dose scanning protocol. To evaluate the model, we collected four different test sets retrospectively to investigate the effects of the shifts in the data characteristics on the model's performance. Among the test cases, there were CT scans with similar characteristics as the train set as well as noisy low-dose and ultra-low dose CT scans. In addition, some test CT scans were obtained from patients with a history of cardiovascular diseases or surgeries. The entire test dataset used in this study contained 51 COVID-19, 28 CAP, and 51 Normal cases. Experimental results indicate that our proposed framework performs well on all test sets achieving total accuracy of 96.15% (95%CI: [91.25-98.74]), COVID-19 sensitivity of 96.08% (95%CI: [86.54-99.5]), CAP sensitivity of 92.86% (95%CI: [76.50-99.19]).

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