Pneumonia Identification with Self-supervised Learning and Transfer Learning

Abstract

Pneumonia is a common lung disease. Diagnosis through X-rays and computed tomography images is currently the best method for diagnosing pneumonia, but it requires the professional judgment of radiologists. However, with the rise of deep learning, convolutional neural networks can be accurately applied to image recognition and processing, laying the foundation for rapid disease diagnosis, which will ensure that patients receive timely treatment and save them time and money. In this paper, we applied self-supervised learning and transfer learning for high-level semantic features of pictures and for pneumonia identification, in order to learn more discriminative representations of images and improve the expressive ability of the network to identify pneumonia. The experimental result shows that both the accuracy and efficiency of network to identify pneumonia have been improved after applying self-supervised learning and transfer learning. The improvements in time and performance of the pneumonia identification task are of great value in reality, which also make this research full of meaning.