Securing healthcare medical image information using advance morphological component analysis, information hiding systems, and hybrid convolutional neural networks on IoMT.

IF 7 2区 医学 Q1 BIOLOGY
Binay Kumar Pandey, Digvijay Pandey
{"title":"Securing healthcare medical image information using advance morphological component analysis, information hiding systems, and hybrid convolutional neural networks on IoMT.","authors":"Binay Kumar Pandey, Digvijay Pandey","doi":"10.1016/j.compbiomed.2024.109499","DOIUrl":null,"url":null,"abstract":"<p><p>Health care images contain a variety of imaging information that has specific features, which can make it challenging to assess and decide on the methods necessitated to safeguard the highly classified visuals from unauthorized exposure during transmission in a communication channel. As a result, this proposed approach utilizes a variety of techniques that will enhance the quality of textual healthcare images, communicate information securely, and interpret textual data from healthcare visuals without difficulty. Natural interference, primarily on the receiver side, reduces text-based healthcare image contrast, and numerous artifacts and adjacent picture element values impede diagnosis. Therefore, at the transmission end, the suggested method uses morphological component analysis to improve the contrast of textual healthcare images. Subsequently, it masks this textual healthcare image behind the cover image using steganography, maintaining the secrecy of private information during transmission on the Internet of Medical Things (IoMT) network. After obtaining the stego-image, reverse steganography is used to separate the textual health care image from the cover image. Following that, pre-processing had been performed utilizing weighted guided image filters to ensure that a text-based health care image would not be altered when data had been sent through an IoMT. After that, the Gabor Transform (GT) and stroke width transform are then used to extract the features required for a weighted classification approach that distinguishes between healthcare images with and without text content. Employing the cultural emperor penguin optimization strategy strengthened the performance of the weighted naive Bayes classifier. Later, a hybrid convolutional neural network with enhanced cuckoo search optimization is utilized to detect textual information in healthcare images. A variety of indicators are utilized to evaluate each cover picture and text-based healthcare image. These are accuracy, precision, recall, sensitivity, specificity, structural similarity index, peak signal-to-noise ratio, number of bytes of embedded and recovered input health-care textual pictures, and mean square error. The findings show that the proposed strategy outperforms all of the existing approaches. The suggested method successfully retrieves content at the receiver end. However, a few characters may be misplaced or recovered many times due to weighted guided image filtration halo artifacts, which impair image quality and provide inaccurate textual data.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"185 ","pages":"109499"},"PeriodicalIF":7.0000,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers in biology and medicine","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1016/j.compbiomed.2024.109499","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Health care images contain a variety of imaging information that has specific features, which can make it challenging to assess and decide on the methods necessitated to safeguard the highly classified visuals from unauthorized exposure during transmission in a communication channel. As a result, this proposed approach utilizes a variety of techniques that will enhance the quality of textual healthcare images, communicate information securely, and interpret textual data from healthcare visuals without difficulty. Natural interference, primarily on the receiver side, reduces text-based healthcare image contrast, and numerous artifacts and adjacent picture element values impede diagnosis. Therefore, at the transmission end, the suggested method uses morphological component analysis to improve the contrast of textual healthcare images. Subsequently, it masks this textual healthcare image behind the cover image using steganography, maintaining the secrecy of private information during transmission on the Internet of Medical Things (IoMT) network. After obtaining the stego-image, reverse steganography is used to separate the textual health care image from the cover image. Following that, pre-processing had been performed utilizing weighted guided image filters to ensure that a text-based health care image would not be altered when data had been sent through an IoMT. After that, the Gabor Transform (GT) and stroke width transform are then used to extract the features required for a weighted classification approach that distinguishes between healthcare images with and without text content. Employing the cultural emperor penguin optimization strategy strengthened the performance of the weighted naive Bayes classifier. Later, a hybrid convolutional neural network with enhanced cuckoo search optimization is utilized to detect textual information in healthcare images. A variety of indicators are utilized to evaluate each cover picture and text-based healthcare image. These are accuracy, precision, recall, sensitivity, specificity, structural similarity index, peak signal-to-noise ratio, number of bytes of embedded and recovered input health-care textual pictures, and mean square error. The findings show that the proposed strategy outperforms all of the existing approaches. The suggested method successfully retrieves content at the receiver end. However, a few characters may be misplaced or recovered many times due to weighted guided image filtration halo artifacts, which impair image quality and provide inaccurate textual data.

求助全文
约1分钟内获得全文 求助全文
来源期刊
Computers in biology and medicine
Computers in biology and medicine 工程技术-工程:生物医学
CiteScore
11.70
自引率
10.40%
发文量
1086
审稿时长
74 days
期刊介绍: Computers in Biology and Medicine is an international forum for sharing groundbreaking advancements in the use of computers in bioscience and medicine. This journal serves as a medium for communicating essential research, instruction, ideas, and information regarding the rapidly evolving field of computer applications in these domains. By encouraging the exchange of knowledge, we aim to facilitate progress and innovation in the utilization of computers in biology and medicine.
×
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