Enhancing COVID-19 prediction using transfer learning from Chest X-ray images
Phuoc-Hai Huynh, Trung-Nguyen Tran, Van Hoa Nguyen
Abstract
The pandemic of COVID-19 is expansion and effect for human lives all over the world. Although many countries have been vaccinated, the number of new COVID-19 patients infected is still increasing. Recently, the detection of COVID-19 early can help find effective treatment plans using machine learning technologies algorithms. We propose the transfer learning models to detect pneumonia disease by this virus from chest X-Ray images. The public dataset is used in this work, and the new chest X-Ray images of COVID-19 patients are collected by An Giang Regional General Hospital. These images enrich the current public dataset and improve the performance prediction. Six transfer learning architectures are investigated using locally collected and public dataset. The experiment results show that the DenseNet121 transfer learning model outperforms others with the accuracy, precision, recall, F1-scores, and AUC of 98.51%, 98.54%, 98.51%, 98.05% and 99.15%, respectively on the augmented dataset and most algorithms process new data are improved performance.