Washington W. Azevedo, Sidney M. L. Lima, Isabella M. M. Fernandes, A. D. D. Rocha, F. Cordeiro, Abel G. da Silva Filho, W. Santos
{"title":"模糊形态学极限学习机检测和分类乳房x光片肿块","authors":"Washington W. Azevedo, Sidney M. L. Lima, Isabella M. M. Fernandes, A. D. D. Rocha, F. Cordeiro, Abel G. da Silva Filho, W. Santos","doi":"10.1109/FUZZ-IEEE.2015.7337975","DOIUrl":null,"url":null,"abstract":"According to the World Health Organization, breast cancer is the most common type of cancer in women. It is also the second leading cause of death among women around the world, becoming the most fatal form of cancer. However, to detect and classify masses is a hard task even for experts. Therefore, due to medical experience, different diagnoses to an image are commonly found. The use of a computer assisted diagnosis is important to avoid misdiagnoses. In this work, we propose Fuzzy Morphological Extreme Learning Machines, with hidden layer kernel based on nonlinear morphological operators of erosion and dilation. The proposed methods were evaluated using 2.796 images from IRMA database, considering fat, fibroid, dense and extremely dense tissues. Zernike Moments and Haralick texture features are used as image descriptors. The proposed model classifies masses as benign, malignant or normal. Results shows comparison between Extreme Learning Machines using Sigmoid and Fuzzy Morphological Kernels, evaluated using classification rate and Kappa index. When using fuzzy morphological kernels, classification rate and Kappa value increases for most of cases analyzed.","PeriodicalId":185191,"journal":{"name":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"27","resultStr":"{\"title\":\"Fuzzy Morphological Extreme Learning Machines to detect and classify masses in mammograms\",\"authors\":\"Washington W. Azevedo, Sidney M. L. Lima, Isabella M. M. Fernandes, A. D. D. Rocha, F. Cordeiro, Abel G. da Silva Filho, W. Santos\",\"doi\":\"10.1109/FUZZ-IEEE.2015.7337975\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"According to the World Health Organization, breast cancer is the most common type of cancer in women. It is also the second leading cause of death among women around the world, becoming the most fatal form of cancer. However, to detect and classify masses is a hard task even for experts. Therefore, due to medical experience, different diagnoses to an image are commonly found. The use of a computer assisted diagnosis is important to avoid misdiagnoses. In this work, we propose Fuzzy Morphological Extreme Learning Machines, with hidden layer kernel based on nonlinear morphological operators of erosion and dilation. The proposed methods were evaluated using 2.796 images from IRMA database, considering fat, fibroid, dense and extremely dense tissues. Zernike Moments and Haralick texture features are used as image descriptors. The proposed model classifies masses as benign, malignant or normal. Results shows comparison between Extreme Learning Machines using Sigmoid and Fuzzy Morphological Kernels, evaluated using classification rate and Kappa index. When using fuzzy morphological kernels, classification rate and Kappa value increases for most of cases analyzed.\",\"PeriodicalId\":185191,\"journal\":{\"name\":\"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"27\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FUZZ-IEEE.2015.7337975\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZ-IEEE.2015.7337975","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fuzzy Morphological Extreme Learning Machines to detect and classify masses in mammograms
According to the World Health Organization, breast cancer is the most common type of cancer in women. It is also the second leading cause of death among women around the world, becoming the most fatal form of cancer. However, to detect and classify masses is a hard task even for experts. Therefore, due to medical experience, different diagnoses to an image are commonly found. The use of a computer assisted diagnosis is important to avoid misdiagnoses. In this work, we propose Fuzzy Morphological Extreme Learning Machines, with hidden layer kernel based on nonlinear morphological operators of erosion and dilation. The proposed methods were evaluated using 2.796 images from IRMA database, considering fat, fibroid, dense and extremely dense tissues. Zernike Moments and Haralick texture features are used as image descriptors. The proposed model classifies masses as benign, malignant or normal. Results shows comparison between Extreme Learning Machines using Sigmoid and Fuzzy Morphological Kernels, evaluated using classification rate and Kappa index. When using fuzzy morphological kernels, classification rate and Kappa value increases for most of cases analyzed.