Pneumonia detection research based on fine-grained image classification

Abstract

In recent years, the novel coronavirus has been raging around the world, which has brought a huge impact on the lives and health of people around the world and social and economic development. Therefore, the research on pneumonia detection methods is of great significance. At present, the pneumonia detection method based on deep learning has attracted the attention of scholars at home and abroad because of its excellent effect, low cost, and fast detection speed; however, there are still many problems in this technical field, such as insufficient detection accuracy, slow response speed, detection Poor reliability, no end-to-end implementation of segmentation and detection; this paper proposes a binary tree cycle attention convolutional neural network (BRA- CNN), the network can identify two discriminative regions at the same time at each layer, forming a binary tree structure, which greatly reduces the disadvantages of the complete failure of the rear network due to the recognition error of the front network, and designed a dedicated loss function for the network, make the network training process go smoothly.