{"title":"基于深度学习的肺结节分类","authors":"Tomoki Kwajiri, Taro Tezuka","doi":"10.11239/JSMBE.55ANNUAL.516","DOIUrl":null,"url":null,"abstract":"Deep learning methods such as the Convolutional Neural Network and the Residual Network were applied to CT scan images in order to classify whether lung nodules become cancerous or not. Especially, the effect of changing the number of layers in the Residual Network was. Experiment were carried out using several models having these two network architectures and consisting of different numbers of layers and parameters.","PeriodicalId":39233,"journal":{"name":"Transactions of Japanese Society for Medical and Biological Engineering","volume":"24 1","pages":"516-517"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Classification of Lung Nodules Using Deep Learning\",\"authors\":\"Tomoki Kwajiri, Taro Tezuka\",\"doi\":\"10.11239/JSMBE.55ANNUAL.516\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Deep learning methods such as the Convolutional Neural Network and the Residual Network were applied to CT scan images in order to classify whether lung nodules become cancerous or not. Especially, the effect of changing the number of layers in the Residual Network was. Experiment were carried out using several models having these two network architectures and consisting of different numbers of layers and parameters.\",\"PeriodicalId\":39233,\"journal\":{\"name\":\"Transactions of Japanese Society for Medical and Biological Engineering\",\"volume\":\"24 1\",\"pages\":\"516-517\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-05-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transactions of Japanese Society for Medical and Biological Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.11239/JSMBE.55ANNUAL.516\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transactions of Japanese Society for Medical and Biological Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11239/JSMBE.55ANNUAL.516","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Engineering","Score":null,"Total":0}
Classification of Lung Nodules Using Deep Learning
Deep learning methods such as the Convolutional Neural Network and the Residual Network were applied to CT scan images in order to classify whether lung nodules become cancerous or not. Especially, the effect of changing the number of layers in the Residual Network was. Experiment were carried out using several models having these two network architectures and consisting of different numbers of layers and parameters.