Transfer Learning-based Mobile-focused Automated COVID-19 Detection from Chest X-ray

COVID-19 Pandemic is still a global issue that threatens global health. To combat the pandemic, testing activities has been the first line of defense. However, increasing number of infections resulted in insufficient number of laboratory kits to perform the test. One potential testing method is using transfer learning for automated detection of COVID-19 from chest x-ray image. We create a model used pretrained model of MobileNetV3Large as a feature extractor, and a custom classification layer. We train the model on dataset consisting of chest x-ray image from 10,192 healthy cases, 3,616 COVID-19 cases, 1,345 Viral Pneumonia cases, and 6,012 Lung Opacity cases. The model achieved macro-average accuracy performance of 89.08%, F1 score of 88.10%, Precision of 91.95%, Sensitivity of 85.51%, and Specificity of 95.26%. Comparison with previous models trained on smaller dataset showed that achieved performance is lower and indicates previous research’s model won’t be able to maintain its performance when evaluated on larger sets of data.