1. Deep-learning based detection of COVID-19 using lung ultrasound imagery
- Author
-
Alejandro Galaviz-Mosqueda, Raul Rivera-Rodriguez, Julia Diaz-Escobar, Vitaly Kober, Nelson E. Ordonez-Guillen, Jose E Lozano Rizk, and Salvador Villarreal-Reyes
- Subjects
Viral Diseases ,Pulmonology ,Wilcoxon signed-rank test ,Computer science ,computer.software_genre ,Diagnostic Radiology ,Medical Conditions ,Ultrasound Imaging ,Image Processing, Computer-Assisted ,Medicine and Health Sciences ,Lung ,Tomography ,Ultrasonography ,Virus Testing ,Multidisciplinary ,Radiology and Imaging ,Ultrasound ,Thorax ,Pulmonary Imaging ,Bone Imaging ,Infectious Diseases ,Pleurae ,Medicine ,Analysis of variance ,Anatomy ,Research Article ,Coronavirus disease 2019 (COVID-19) ,Imaging Techniques ,Science ,Neuroimaging ,Research and Analysis Methods ,Machine learning ,Deep Learning ,Diagnostic Medicine ,medicine ,Humans ,Receiver operating characteristic ,business.industry ,Deep learning ,COVID-19 ,Biology and Life Sciences ,Covid 19 ,Pneumonia ,medicine.disease ,Computed Axial Tomography ,Lung ultrasound ,X-Ray Radiography ,Artificial intelligence ,business ,computer ,Neuroscience - Abstract
BackgroundThe COVID-19 pandemic has exposed the vulnerability of healthcare services worldwide, especially in underdeveloped countries. There is a clear need to develop novel computer-assisted diagnosis tools to provide rapid and cost-effective screening in places where massive traditional testing is not feasible. Lung ultrasound is a portable, easy to disinfect, low cost and non-invasive tool that can be used to identify lung diseases. Computer-assisted analysis of lung ultrasound imagery is a relatively recent approach that has shown great potential for diagnosing pulmonary conditions, being a viable alternative for screening and diagnosing COVID-19.ObjectiveTo evaluate and compare the performance of deep-learning techniques for detecting COVID-19 infections from lung ultrasound imagery.MethodsWe adapted different pre-trained deep learning architectures, including VGG19, InceptionV3, Xception, and ResNet50. We used the publicly available POCUS dataset comprising 3326 lung ultrasound frames of healthy, COVID-19, and pneumonia patients for training and fine-tuning. We conducted two experiments considering three classes (COVID-19, pneumonia, and healthy) and two classes (COVID-19 versus pneumonia and COVID-19 versus non-COVID-19) of predictive models. The obtained results were also compared with the POCOVID-net model. For performance evaluation, we calculated per-class classification metrics (Precision, Recall, and F1-score) and overall metrics (Accuracy, Balanced Accuracy, and Area Under the Receiver Operating Characteristic Curve). Lastly, we performed a statistical analysis of performance results using ANOVA and Friedman tests followed by post-hoc analysis using the Wilcoxon signed-rank test with the Holm’s step-down correction.ResultsInceptionV3 network achieved the best average accuracy (89.1%), balanced accuracy (89.3%), and area under the receiver operating curve (97.1%) for COVID-19 detection from bacterial pneumonia and healthy lung ultrasound data. The ANOVA and Friedman tests found statistically significant performance differences between models for accuracy, balanced accuracy and area under the receiver operating curve. Post-hoc analysis showed statistically significant differences between the performance obtained with the InceptionV3-based model and POCOVID-net, VGG19-, and ResNet50-based models. No statistically significant differences were found in the performance obtained with InceptionV3- and Xception-based models.ConclusionsDeep learning techniques for computer-assisted analysis of lung ultrasound imagery provide a promising avenue for COVID-19 screening and diagnosis. Particularly, we found that the InceptionV3 network provides the most promising predictive results from all AI-based techniques evaluated in this work. InceptionV3- and Xception-based models can be used to further develop a viable computer-assisted screening tool for COVID-19 based on ultrasound imagery.
- Published
- 2021
- Full Text
- View/download PDF