Liang Bing-jin, Yin Jian, Lin Yan-jun, Pan Liang, Lin Guo-xiong
{"title":"基于DenseNet的x线胸片疾病分类研究与实践","authors":"Liang Bing-jin, Yin Jian, Lin Yan-jun, Pan Liang, Lin Guo-xiong","doi":"10.1109/ICAIE50891.2020.00063","DOIUrl":null,"url":null,"abstract":"X-ray examination, commonly used to examine chest diseases, is a common medical examination in patients’ physical examination. In this paper, the disease information is extracted based on the X-ray chest film report, and the disease types are classified by using DenseNet. The highest AUC can reach more than 0.935, the positive recall rate is about 0.82, and the negative recall rate is about 0.92, which has a good effect.","PeriodicalId":164823,"journal":{"name":"2020 International Conference on Artificial Intelligence and Education (ICAIE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research and Practice of X-ray Chest Film Disease Classification based on DenseNet\",\"authors\":\"Liang Bing-jin, Yin Jian, Lin Yan-jun, Pan Liang, Lin Guo-xiong\",\"doi\":\"10.1109/ICAIE50891.2020.00063\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"X-ray examination, commonly used to examine chest diseases, is a common medical examination in patients’ physical examination. In this paper, the disease information is extracted based on the X-ray chest film report, and the disease types are classified by using DenseNet. The highest AUC can reach more than 0.935, the positive recall rate is about 0.82, and the negative recall rate is about 0.92, which has a good effect.\",\"PeriodicalId\":164823,\"journal\":{\"name\":\"2020 International Conference on Artificial Intelligence and Education (ICAIE)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Conference on Artificial Intelligence and Education (ICAIE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAIE50891.2020.00063\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Artificial Intelligence and Education (ICAIE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAIE50891.2020.00063","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research and Practice of X-ray Chest Film Disease Classification based on DenseNet
X-ray examination, commonly used to examine chest diseases, is a common medical examination in patients’ physical examination. In this paper, the disease information is extracted based on the X-ray chest film report, and the disease types are classified by using DenseNet. The highest AUC can reach more than 0.935, the positive recall rate is about 0.82, and the negative recall rate is about 0.92, which has a good effect.