COVID-19 ResNet: Residual neural network for COVID-19 classification with bayesian data augmentation
COVID-19 is an infectious disease caused by a novel coronavirus called SARS-CoV-2. The first case appeared in December 2019, and until now it still represents a significant challenge to many countries in the world. Accurately detecting positive COVID-19 patients is a crucial step to reduce the spre...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Universidad San Francisco de Quito USFQ
2021-11-01
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| Series: | ACI Avances en Ciencias e Ingenierías |
| Subjects: | |
| Online Access: | https://revistas.usfq.edu.ec/index.php/avances/article/view/2288 |
| Summary: | COVID-19 is an infectious disease caused by a novel coronavirus called SARS-CoV-2. The first case appeared in December 2019, and until now it still represents a significant challenge to many countries in the world. Accurately detecting positive COVID-19 patients is a crucial step to reduce the spread of the disease, which is characterize by a strong transmission capacity. In this work we implement a Residual Convolutional Neural Network (ResNet) for an automated COVID-19 diagnosis. The implemented ResNet can classify a patient´s Chest-Xray image into COVID-19 positive, pneumonia caused from another virus or bacteria, and healthy. Moreover, to increase the accuracy of the model and overcome the data scarcity of COVID-19 images, a personalized data augmentation strategy using a three-step Bayesian hyperparameter optimization approach is applied to enrich the dataset during the training process. The proposed COVID-19 ResNet achieves a 94% accuracy, 95% recall, and 95% F1-score in test set. Furthermore, we also provide an insight into which data augmentation operations are successful in increasing a CNNs performance when doing medical image classification with COVID-19 CXR.
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| ISSN: | 1390-5384 2528-7788 |