基于卷积深度神经网络的聚类图像和特征支持分类器(CIFC)技术脑肿瘤诊断。

IF 1 4区生物学 Q3 BIOLOGY

Brazilian Archives of Biology and Technology Pub Date : 2023-01-01 DOI:10.1590/1678-4324-2023230012

Parameswari Alagarsamy, Bhavani Sridharan, Vinoth Kumar Kalimuthu

{"title":"基于卷积深度神经网络的聚类图像和特征支持分类器(CIFC)技术脑肿瘤诊断。","authors":"Parameswari Alagarsamy, Bhavani Sridharan, Vinoth Kumar Kalimuthu","doi":"10.1590/1678-4324-2023230012","DOIUrl":null,"url":null,"abstract":"HIGHLIGHTS Medical image analysis research has been performed to aid in the detection of malignant brain tumors. OSFC, OSIC and CIFC performed well and produced better results in classification. The performance metric outcome of the proposed system is 99.76% of sensitivity, 98.04% of specificity and 99.87% of accuracy.","PeriodicalId":9169,"journal":{"name":"Brazilian Archives of Biology and Technology","volume":"5 1","pages":"0"},"PeriodicalIF":1.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Convolutional Deep Neural Network Based Brain TumorDiagnoses Using Clustered Image and Feature-Supported Classifier (CIFC)Technique.\",\"authors\":\"Parameswari Alagarsamy, Bhavani Sridharan, Vinoth Kumar Kalimuthu\",\"doi\":\"10.1590/1678-4324-2023230012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"HIGHLIGHTS Medical image analysis research has been performed to aid in the detection of malignant brain tumors. OSFC, OSIC and CIFC performed well and produced better results in classification. The performance metric outcome of the proposed system is 99.76% of sensitivity, 98.04% of specificity and 99.87% of accuracy.\",\"PeriodicalId\":9169,\"journal\":{\"name\":\"Brazilian Archives of Biology and Technology\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Brazilian Archives of Biology and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1590/1678-4324-2023230012\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Brazilian Archives of Biology and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1590/1678-4324-2023230012","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOLOGY","Score":null,"Total":0}

引用次数: 0

摘要

医学图像分析研究已经进行，以帮助检测恶性脑肿瘤。OSFC、OSIC和CIFC表现较好，分类效果较好。该系统的灵敏度为99.76%，特异性为98.04%，准确度为99.87%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A Convolutional Deep Neural Network Based Brain TumorDiagnoses Using Clustered Image and Feature-Supported Classifier (CIFC)Technique.

HIGHLIGHTS Medical image analysis research has been performed to aid in the detection of malignant brain tumors. OSFC, OSIC and CIFC performed well and produced better results in classification. The performance metric outcome of the proposed system is 99.76% of sensitivity, 98.04% of specificity and 99.87% of accuracy.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Brazilian Archives of Biology and Technology 生物-生物学

CiteScore

1.80

自引率

0.00%

发文量

116

审稿时长

3 months

期刊介绍： Information not localized