COVID-19 and Pneumonia Recognition based on Data Augmentation and Transfer Learning

Li, Zhe

Up to now, COVID-19 has been diagnosed in the world as many as about sixty million, which has caused tremendous pressure and burden to hospitals and medical systems. The number of chest X-rays required to be reviewed by doctors of relevant specialties is more than ever before. Besides, in the process of recognition, there are many difficulties for human identification of chest X-ray images, such as the naked eye is difficult to find tiny abnormalities, the chest X-ray images researchers have is limited and doctors is unfamiliar with this new disease. In this case, it is very necessary to use deep learning to help doctors diagnose pneumonia and solve some urgent difficulties. In the field of deep learning, transfer learning focuses on saving solution model of previous problems, and takes advantage of it on other different but related problems. Therefore, we can use the existed Imagenet model to help us train the dataset of COVID-19 and pneumonia. Moreover, deep learning depends on a large amount of data, but when the data is few, data augmentation is able to increase data by some methods, so it is a effective way to overcome the lack of train data. In terms of the good function of these two ways, we use them to improve performance and help doctors improve diagnosis so as to relieve the current tense medical situation.