A Deep Learning Architecture for Meningioma Brain Tumor Detection and Segmentation.

IF 2.5 Q3 ONCOLOGY
John Nisha Anita, Sujatha Kumaran
{"title":"A Deep Learning Architecture for Meningioma Brain Tumor Detection and Segmentation.","authors":"John Nisha Anita,&nbsp;Sujatha Kumaran","doi":"10.15430/JCP.2022.27.3.192","DOIUrl":null,"url":null,"abstract":"<p><p>The meningioma brain tumor detection and segmentation method is a complex process due to its low intensity pixel profile. In this article, the meningioma brain tumor images were detected and tumor regions were segmented using a convolutional neural network (CNN) classification approach. The source brain MRI images were decomposed using the discrete wavelet transform and these decomposed sub bands were fused using an arithmetic fusion technique. The fused image was data augmented in order to increase the sample size. The data augmented images were classified into either healthy or malignant using a CNN classifier. Then, the tumor region in the classified meningioma brain image was segmented using an connection component analysis algorithm. The tumor region segmented meningioma brain image was compressed using a lossless compression technique. The proposed method stated in this article was experimentally tested with the sets of meningioma brain images from an open access dataset. The experimental results were compared with existing methods in terms of sensitivity, specificity and tumor segmentation accuracy.</p>","PeriodicalId":15120,"journal":{"name":"Journal of Cancer Prevention","volume":"27 3","pages":"192-198"},"PeriodicalIF":2.5000,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/26/b2/jcp-27-3-192.PMC9537580.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Cancer Prevention","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15430/JCP.2022.27.3.192","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

The meningioma brain tumor detection and segmentation method is a complex process due to its low intensity pixel profile. In this article, the meningioma brain tumor images were detected and tumor regions were segmented using a convolutional neural network (CNN) classification approach. The source brain MRI images were decomposed using the discrete wavelet transform and these decomposed sub bands were fused using an arithmetic fusion technique. The fused image was data augmented in order to increase the sample size. The data augmented images were classified into either healthy or malignant using a CNN classifier. Then, the tumor region in the classified meningioma brain image was segmented using an connection component analysis algorithm. The tumor region segmented meningioma brain image was compressed using a lossless compression technique. The proposed method stated in this article was experimentally tested with the sets of meningioma brain images from an open access dataset. The experimental results were compared with existing methods in terms of sensitivity, specificity and tumor segmentation accuracy.

Abstract Image

Abstract Image

Abstract Image

脑膜瘤检测与分割的深度学习架构。
脑膜瘤由于其低强度的像素轮廓,其检测和分割方法是一个复杂的过程。本文采用卷积神经网络(CNN)分类方法对脑膜瘤图像进行检测,并对肿瘤区域进行分割。采用离散小波变换对脑MRI源图像进行分解,并采用算法融合技术对分解后的子带进行融合。对融合后的图像进行数据增强,以增加样本量。使用CNN分类器将数据增强图像分为健康或恶性。然后,利用连接分量分析算法对分类脑膜瘤脑图像中的肿瘤区域进行分割。采用无损压缩技术对肿瘤区域分割的脑膜瘤脑图像进行压缩。本文中提出的方法通过实验测试了来自开放获取数据集的脑膜瘤脑图像集。将实验结果与现有方法在敏感性、特异性和肿瘤分割准确率方面进行比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
4.00%
发文量
32
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信