文献类型：期刊文献

英文题名：CXR-RefineDet: Single-Shot Refinement Neural Network for Chest X-Ray Radiograph Based on Multiple Lesions Detection

作者：Lin, Cong[1];Zheng, Yongbin[1];Xiao, Xiuchun[1];Lin, Jialun[2]

机构：[1]Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang 524025, Peoples R China;[2]Hainan Med Univ, Coll Biomed Informat & Engn, Haikou 571199, Hainan, Peoples R China

年份：2022

卷号：2022

外文期刊名：JOURNAL OF HEALTHCARE ENGINEERING

收录：SCI-EXPANDED(收录号：WOS:000783312700022)、、EI(收录号：20220411496493)、Scopus(收录号：2-s2.0-85123051892)、WOS

基金：This work was supported by the National Natural Science Foundation of China under Grant no. 62072121, Natural Science Foundation of Guangdong Province (2021A1515011847), and Higher Education Reform Key Project of Hainan Province (Hnjg2018ZD-11).

语种：英文

外文关键词：Biological organs - Radiography - Inference engines - Object recognition - Information retrieval - Medical imaging - Computer aided diagnosis - Object detection

外文摘要：The workload of radiologists has dramatically increased in the context of the COVID-19 pandemic, causing misdiagnosis and missed diagnosis of diseases. The use of artificial intelligence technology can assist doctors in locating and identifying lesions in medical images. In order to improve the accuracy of disease diagnosis in medical imaging, we propose a lung disease detection neural network that is superior to the current mainstream object detection model in this paper. By combining the advantages of RepVGG block and Resblock in information fusion and information extraction, we design a backbone RRNet with few parameters and strong feature extraction capabilities. After that, we propose a structure called Information Reuse, which can solve the problem of low utilization of the original network output features by connecting the normalized features back to the network Combining the network of RRNet and the improved RefineDet, we propose the overall network which was called CXR-RefineDet. Through a large number of experiments on the largest public lung chest radiograph detection dataset VinDr-CXR, it is found that the detection accuracy and inference speed of CXR-RefineDet have reached 0.1686 mAP and 6.8 fps, respectively, which is better than the two-stage object detection algorithm using a strong backbone like ResNet-50 and ResNet-101. In addition, the fast reasoning speed of CXR-RefineDet also provides the possibility for the actual implementation of the computer-aided diagnosis system.