{"title":"基于卷积神经网络的肺结节检测与分类","authors":"N. Abbadi","doi":"10.37896/jxat12.05/1546","DOIUrl":null,"url":null,"abstract":"- Lung cancer is one of the most dangerous types among the other type of cancer because is leading to death. The doctors face many difficulties to interpret and identify the cancer from CT-scan images. Therefore, computer aided using image processing and machine learning can be useful for early detection lung nodules and classify to benign or malignant with highly accuracy, less effort and reasonable time. In this proposal convolution neural network suggested to detect and identify the nodules and classify to benign or malignant. Network trained on more than 5000 images and tested with 606 images. The results were highly promised and the network classified all the tested images.","PeriodicalId":35514,"journal":{"name":"西安建筑科技大学学报(自然科学版)","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Lung Nodules Detection and Classification Using Convolution Neural Network\",\"authors\":\"N. Abbadi\",\"doi\":\"10.37896/jxat12.05/1546\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"- Lung cancer is one of the most dangerous types among the other type of cancer because is leading to death. The doctors face many difficulties to interpret and identify the cancer from CT-scan images. Therefore, computer aided using image processing and machine learning can be useful for early detection lung nodules and classify to benign or malignant with highly accuracy, less effort and reasonable time. In this proposal convolution neural network suggested to detect and identify the nodules and classify to benign or malignant. Network trained on more than 5000 images and tested with 606 images. The results were highly promised and the network classified all the tested images.\",\"PeriodicalId\":35514,\"journal\":{\"name\":\"西安建筑科技大学学报(自然科学版)\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"西安建筑科技大学学报(自然科学版)\",\"FirstCategoryId\":\"1087\",\"ListUrlMain\":\"https://doi.org/10.37896/jxat12.05/1546\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"西安建筑科技大学学报(自然科学版)","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.37896/jxat12.05/1546","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
Lung Nodules Detection and Classification Using Convolution Neural Network
- Lung cancer is one of the most dangerous types among the other type of cancer because is leading to death. The doctors face many difficulties to interpret and identify the cancer from CT-scan images. Therefore, computer aided using image processing and machine learning can be useful for early detection lung nodules and classify to benign or malignant with highly accuracy, less effort and reasonable time. In this proposal convolution neural network suggested to detect and identify the nodules and classify to benign or malignant. Network trained on more than 5000 images and tested with 606 images. The results were highly promised and the network classified all the tested images.
期刊介绍:
Journal of Xi'an University of Architecture and Technology (Natural Science Edition) is referred to as the Natural Science Edition. It is publicly distributed at home and abroad, bimonthly, ISSN1006-7930, CN61-1295/TU. Founded in February 1957, it is a comprehensive academic journal focusing on academic papers on basic research and applied research in related disciplines such as architecture and civil engineering.
The Natural Science Edition is one of the top 100 scientific and technological journals of Chinese universities and a high-quality journal of Shaanxi universities. It is a Chinese core journal (Peking University core), a Chinese science and technology core journal, a T2 journal in the classification catalog of high-quality scientific and technological journals in the field of architectural science, and an authoritative academic journal in China by the China Center for Science Evaluation (RCCSE).