{"title":"基于Lasso-BP神经网络的乳腺肿瘤检测研究","authors":"Yanrong Zhang, Lingyue Meng, Yan Liu, Jiayuan Sun","doi":"10.1109/ICAICA50127.2020.9182487","DOIUrl":null,"url":null,"abstract":"In recent years, breast cancer, as one of the most threatening tumors for women's health in China, affects women's health with a growth rate of 2% every year. The Lasso algorithm was used to screen the characteristics of breast cancer data, and then the BP neural network was used to classify the 9 breast cancer data determination factors in the UCI dataset and the remaining 8 determination factors after screening. The experimental results showed that: In the detection of breast cancer based on BP neural network, the remaining 8 breast cancer data features are used to classify benign and malignant tumors, and the classification accuracy rate is higher than that of the original 9 breast cancer data features.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Study on Breast Tumor Detection Based on Lasso-BP Neural Network\",\"authors\":\"Yanrong Zhang, Lingyue Meng, Yan Liu, Jiayuan Sun\",\"doi\":\"10.1109/ICAICA50127.2020.9182487\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, breast cancer, as one of the most threatening tumors for women's health in China, affects women's health with a growth rate of 2% every year. The Lasso algorithm was used to screen the characteristics of breast cancer data, and then the BP neural network was used to classify the 9 breast cancer data determination factors in the UCI dataset and the remaining 8 determination factors after screening. The experimental results showed that: In the detection of breast cancer based on BP neural network, the remaining 8 breast cancer data features are used to classify benign and malignant tumors, and the classification accuracy rate is higher than that of the original 9 breast cancer data features.\",\"PeriodicalId\":113564,\"journal\":{\"name\":\"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)\",\"volume\":\"75 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 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAICA50127.2020.9182487\",\"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 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAICA50127.2020.9182487","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Study on Breast Tumor Detection Based on Lasso-BP Neural Network
In recent years, breast cancer, as one of the most threatening tumors for women's health in China, affects women's health with a growth rate of 2% every year. The Lasso algorithm was used to screen the characteristics of breast cancer data, and then the BP neural network was used to classify the 9 breast cancer data determination factors in the UCI dataset and the remaining 8 determination factors after screening. The experimental results showed that: In the detection of breast cancer based on BP neural network, the remaining 8 breast cancer data features are used to classify benign and malignant tumors, and the classification accuracy rate is higher than that of the original 9 breast cancer data features.