{"title":"基于深度信念网络的新型核极值学习脑肿瘤检测及与模糊c均值聚类的预测精度比较","authors":"V. V. Vardhan Reddy, U. S","doi":"10.1109/ICTACS56270.2022.9988190","DOIUrl":null,"url":null,"abstract":"To identify the brain tumor according to the categorical identification by using the symptoms. Materials and Methods: To identify brain tumor using Kernel Extreme Learning Machine with improved accuracy over Fuzzy C-means clustering. Results: The proposed hybrid Kernel Extreme Learning Machine approach gives accuracy 93.31% which is significantly better in classification when compared to Fuzzy C-means clustering which has less accuracy 80.14%.and level of significance is 0.01 (p<0.05). Conclusion: Identifying brain tumor was achieved significantly better by using Kernel Extreme Learning Machine compared to Fuzzy C-means clustering.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Brain Tumor Detection using Novel Kernel Extreme Learning with Deep Belief Network and Compare Prediction Accuracy with Fuzzy C-means Clustering\",\"authors\":\"V. V. Vardhan Reddy, U. S\",\"doi\":\"10.1109/ICTACS56270.2022.9988190\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To identify the brain tumor according to the categorical identification by using the symptoms. Materials and Methods: To identify brain tumor using Kernel Extreme Learning Machine with improved accuracy over Fuzzy C-means clustering. Results: The proposed hybrid Kernel Extreme Learning Machine approach gives accuracy 93.31% which is significantly better in classification when compared to Fuzzy C-means clustering which has less accuracy 80.14%.and level of significance is 0.01 (p<0.05). Conclusion: Identifying brain tumor was achieved significantly better by using Kernel Extreme Learning Machine compared to Fuzzy C-means clustering.\",\"PeriodicalId\":385163,\"journal\":{\"name\":\"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)\",\"volume\":\"43 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICTACS56270.2022.9988190\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTACS56270.2022.9988190","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Brain Tumor Detection using Novel Kernel Extreme Learning with Deep Belief Network and Compare Prediction Accuracy with Fuzzy C-means Clustering
To identify the brain tumor according to the categorical identification by using the symptoms. Materials and Methods: To identify brain tumor using Kernel Extreme Learning Machine with improved accuracy over Fuzzy C-means clustering. Results: The proposed hybrid Kernel Extreme Learning Machine approach gives accuracy 93.31% which is significantly better in classification when compared to Fuzzy C-means clustering which has less accuracy 80.14%.and level of significance is 0.01 (p<0.05). Conclusion: Identifying brain tumor was achieved significantly better by using Kernel Extreme Learning Machine compared to Fuzzy C-means clustering.