{"title":"一种利用波原子特征和圆形复合体价值的乳房x线微钙化良恶性表征的新方法-极限学习机","authors":"Malar Elangeeran, Savitha Ramasamy, Kandaswamy Arumugam","doi":"10.1109/ISSNIP.2014.6827660","DOIUrl":null,"url":null,"abstract":"This paper presents a novel procedure involving waveatom transform and Circular Complex-valued Extreme Learning Machine (CC-ELM) for automatic characterization of mammographic microcalcifications into benign or malignant. Waveatom transform is used to transform the mammogram image into multi-frequency domain features. The best feature set is obtained by feature reduction through Principal Component Analysis. The reduced feature set is then used to perform classification through a CC-ELM classifier. CC-ELM is a fast learning fully complex-valued classifier to perform real-valued classification tasks efficiently. Mammographic images obtained from Digital Database for Screening Mammography have been used in the study. About 400 Region of Interests extracted from mammograms are used. The performance of the proposed method is about 96.19%, which is significantly higher than the existing methods.","PeriodicalId":269784,"journal":{"name":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"A novel method for benign and malignant characterization of mammographic microcalcifications employing waveatom features and circular complex valued — Extreme Learning Machine\",\"authors\":\"Malar Elangeeran, Savitha Ramasamy, Kandaswamy Arumugam\",\"doi\":\"10.1109/ISSNIP.2014.6827660\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a novel procedure involving waveatom transform and Circular Complex-valued Extreme Learning Machine (CC-ELM) for automatic characterization of mammographic microcalcifications into benign or malignant. Waveatom transform is used to transform the mammogram image into multi-frequency domain features. The best feature set is obtained by feature reduction through Principal Component Analysis. The reduced feature set is then used to perform classification through a CC-ELM classifier. CC-ELM is a fast learning fully complex-valued classifier to perform real-valued classification tasks efficiently. Mammographic images obtained from Digital Database for Screening Mammography have been used in the study. About 400 Region of Interests extracted from mammograms are used. The performance of the proposed method is about 96.19%, which is significantly higher than the existing methods.\",\"PeriodicalId\":269784,\"journal\":{\"name\":\"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-04-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISSNIP.2014.6827660\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSNIP.2014.6827660","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A novel method for benign and malignant characterization of mammographic microcalcifications employing waveatom features and circular complex valued — Extreme Learning Machine
This paper presents a novel procedure involving waveatom transform and Circular Complex-valued Extreme Learning Machine (CC-ELM) for automatic characterization of mammographic microcalcifications into benign or malignant. Waveatom transform is used to transform the mammogram image into multi-frequency domain features. The best feature set is obtained by feature reduction through Principal Component Analysis. The reduced feature set is then used to perform classification through a CC-ELM classifier. CC-ELM is a fast learning fully complex-valued classifier to perform real-valued classification tasks efficiently. Mammographic images obtained from Digital Database for Screening Mammography have been used in the study. About 400 Region of Interests extracted from mammograms are used. The performance of the proposed method is about 96.19%, which is significantly higher than the existing methods.