Transfer Learning for Detecting Covid-19 Cases Using Chest X-Ray Images
Keywords:
Machine learning, Transfer learning, COVID-19, Coronavirus, ImageNetAbstract
The COVID-19 pandemic is a global health crisis that have already infected more than 3.5 million people and caused more than 250 thousand deaths around the globe. That is why it is critical to develop a more efficient way to detect and treat this illness. This paper utilizes transfer learning techniques to detect normal, COVID-19, and viral pneumonia cases from Chest X-Ray images. Four pre-trained models on ImageNet were chosen as the base model, which are ResNet50, VGG19, DenseNet121, and InceptionV3. The performance metrics of each fine-tuned model are overall similar. With an average recall, precision, f1-score, and accuracy of 97.42%, 97.42%, 97.23%, 98.3% respectively.
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