| Autor: |
Furtado, Adhvan, Andrade, Leandro, Frias, Diego, Maia, Thiago, Badaró, Roberto, Nascimento, Erick G. Sperandio |
| Předmět: |
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| Zdroj: |
Applied Sciences (2076-3417); Apr2022, Vol. 12 Issue 8, p3712, 19p |
| Abstrakt: |
Featured Application: The open-source deep learning algorithm presented in this work can identify anomalous chest radiographs and support the detection of COVID-19 cases. It is a complementary tool to support COVID-19 identification in areas with no access to radiology specialists or RT-PCR tests. We encourage the use of the algorithm to support COVID-19 screening, for educational purposes, as a baseline for further enhancements, and as a benchmark for different solutions. The algorithm is currently being tested in clinical practice in a hospital in Espírito Santo, Brazil. Due to the recent COVID-19 pandemic, a large number of reports present deep learning algorithms that support the detection of pneumonia caused by COVID-19 in chest radiographs. Few studies have provided the complete source code, limiting testing and reproducibility on different datasets. This work presents Cimatec_XCOV19, a novel deep learning system inspired by the Inception-V3 architecture that is able to (i) support the identification of abnormal chest radiographs and (ii) classify the abnormal radiographs as suggestive of COVID-19. The training dataset has 44,031 images with 2917 COVID-19 cases, one of the largest datasets in recent literature. We organized and published an external validation dataset of 1158 chest radiographs from a Brazilian hospital. Two experienced radiologists independently evaluated the radiographs. The Cimatec_XCOV19 algorithm obtained a sensitivity of 0.85, specificity of 0.82, and AUC ROC of 0.93. We compared the AUC ROC of our algorithm with a well-known public solution and did not find a statistically relevant difference between both performances. We provide full access to the code and the test dataset, enabling this work to be used as a tool for supporting the fast screening of COVID-19 on chest X-ray exams, serving as a reference for educators, and supporting further algorithm enhancements. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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