设计一种人工神经网络与粒子群优化的混合方法来诊断结肠直肠 CT 图像中的息肉

IF 1.7 Q2 MEDICINE, GENERAL & INTERNAL International Journal of Preventive Medicine Pub Date : 2024-01-31 eCollection Date: 2024-01-01 DOI:10.4103/ijpvm.ijpvm_373_22
Hossein Beigi Harchegani, Hamid Moghaddasi
{"title":"设计一种人工神经网络与粒子群优化的混合方法来诊断结肠直肠 CT 图像中的息肉","authors":"Hossein Beigi Harchegani, Hamid Moghaddasi","doi":"10.4103/ijpvm.ijpvm_373_22","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Since colorectal cancer is one of the most important types of cancer in the world that often leads to death, computer-aided diagnostic (CAD) systems are a promising solution for early diagnosis of this disease with fewer side effects than conventional colonoscopy. Therefore, the aim of this research is to design a CAD system for processing colorectal Computerized Tomography (CT) images using a combination of an artificial neural network and a particle swarm optimizer.</p><p><strong>Method: </strong>First, the data set of the research was created from the colorectal CT images of the patients of Loghman-e Hakim Hospitals in Tehran and Al-Zahra Hospitals in Isfahan who underwent colorectal CT imaging and had conventional colonoscopy done within a maximum period of one month after that. Then the steps of model implementation, including electronic cleansing of images, segmentation, labeling of samples, extraction of features, and training and optimization of the artificial neural network (ANN) with a particle swarm optimizer, were performed. A binomial statistical test and confusion matrix calculation were used to evaluate the model.</p><p><strong>Results: </strong>The values of accuracy, sensitivity, and specificity of the model with a <i>P</i> value = 0.000 as a result of the McNemar test were 0.9354, 0.9298, and 0.9889, respectively. Also, the result of the <i>P</i> value of the binomial test of the ratio of diagnosis of the model and the radiologist from Loqman Hakim and Al-Zahra Hospitals was 0.044 and 0.021, respectively.</p><p><strong>Conclusions: </strong>The results of statistical tests and research variables show the efficiency of the CTC-CAD system created based on the hybrid of the ANN and particle swarm optimization compared to the opinion of radiologists in diagnosing colorectal polyps from CTC images.</p>","PeriodicalId":14342,"journal":{"name":"International Journal of Preventive Medicine","volume":"15 ","pages":"4"},"PeriodicalIF":1.7000,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10935572/pdf/","citationCount":"0","resultStr":"{\"title\":\"Designing a Hybrid Method of Artificial Neural Network and Particle Swarm Optimization to Diagnosis Polyps from Colorectal CT Images.\",\"authors\":\"Hossein Beigi Harchegani, Hamid Moghaddasi\",\"doi\":\"10.4103/ijpvm.ijpvm_373_22\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Since colorectal cancer is one of the most important types of cancer in the world that often leads to death, computer-aided diagnostic (CAD) systems are a promising solution for early diagnosis of this disease with fewer side effects than conventional colonoscopy. Therefore, the aim of this research is to design a CAD system for processing colorectal Computerized Tomography (CT) images using a combination of an artificial neural network and a particle swarm optimizer.</p><p><strong>Method: </strong>First, the data set of the research was created from the colorectal CT images of the patients of Loghman-e Hakim Hospitals in Tehran and Al-Zahra Hospitals in Isfahan who underwent colorectal CT imaging and had conventional colonoscopy done within a maximum period of one month after that. Then the steps of model implementation, including electronic cleansing of images, segmentation, labeling of samples, extraction of features, and training and optimization of the artificial neural network (ANN) with a particle swarm optimizer, were performed. A binomial statistical test and confusion matrix calculation were used to evaluate the model.</p><p><strong>Results: </strong>The values of accuracy, sensitivity, and specificity of the model with a <i>P</i> value = 0.000 as a result of the McNemar test were 0.9354, 0.9298, and 0.9889, respectively. Also, the result of the <i>P</i> value of the binomial test of the ratio of diagnosis of the model and the radiologist from Loqman Hakim and Al-Zahra Hospitals was 0.044 and 0.021, respectively.</p><p><strong>Conclusions: </strong>The results of statistical tests and research variables show the efficiency of the CTC-CAD system created based on the hybrid of the ANN and particle swarm optimization compared to the opinion of radiologists in diagnosing colorectal polyps from CTC images.</p>\",\"PeriodicalId\":14342,\"journal\":{\"name\":\"International Journal of Preventive Medicine\",\"volume\":\"15 \",\"pages\":\"4\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-01-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10935572/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Preventive Medicine\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4103/ijpvm.ijpvm_373_22\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICINE, GENERAL & INTERNAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Preventive Medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4103/ijpvm.ijpvm_373_22","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0

摘要

背景:结肠直肠癌是世界上最重要的癌症类型之一,通常会导致死亡,因此计算机辅助诊断(CAD)系统是早期诊断这种疾病的一个很有前途的解决方案,与传统的结肠镜检查相比,它的副作用更小。因此,本研究的目的是结合人工神经网络和粒子群优化器,设计一种处理结肠直肠计算机断层扫描(CT)图像的计算机辅助诊断系统:首先,研究数据集来自德黑兰 Loghman-e Hakim 医院和伊斯法罕 Al-Zahra 医院的结肠直肠 CT 图像,这些患者接受了结肠直肠 CT 成像检查,并在检查后最长一个月内进行了常规结肠镜检查。随后进行了模型实施步骤,包括图像的电子清洗、分割、样本标记、特征提取,以及使用粒子群优化器对人工神经网络(ANN)进行训练和优化。使用二项式统计检验和混淆矩阵计算对模型进行评估:经 McNemar 检验,在 P 值 = 0.000 的情况下,模型的准确度、灵敏度和特异度分别为 0.9354、0.9298 和 0.9889。此外,模型与 Loqman Hakim 医院和 Al-Zahra 医院放射科医生诊断比率的二项检验 P 值分别为 0.044 和 0.021:统计检验和研究变量的结果表明,在从 CTC 图像诊断结直肠息肉方面,与放射科医生的意见相比,基于 ANN 和粒子群优化混合法创建的 CTC-CAD 系统具有很高的效率。
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
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Designing a Hybrid Method of Artificial Neural Network and Particle Swarm Optimization to Diagnosis Polyps from Colorectal CT Images.

Background: Since colorectal cancer is one of the most important types of cancer in the world that often leads to death, computer-aided diagnostic (CAD) systems are a promising solution for early diagnosis of this disease with fewer side effects than conventional colonoscopy. Therefore, the aim of this research is to design a CAD system for processing colorectal Computerized Tomography (CT) images using a combination of an artificial neural network and a particle swarm optimizer.

Method: First, the data set of the research was created from the colorectal CT images of the patients of Loghman-e Hakim Hospitals in Tehran and Al-Zahra Hospitals in Isfahan who underwent colorectal CT imaging and had conventional colonoscopy done within a maximum period of one month after that. Then the steps of model implementation, including electronic cleansing of images, segmentation, labeling of samples, extraction of features, and training and optimization of the artificial neural network (ANN) with a particle swarm optimizer, were performed. A binomial statistical test and confusion matrix calculation were used to evaluate the model.

Results: The values of accuracy, sensitivity, and specificity of the model with a P value = 0.000 as a result of the McNemar test were 0.9354, 0.9298, and 0.9889, respectively. Also, the result of the P value of the binomial test of the ratio of diagnosis of the model and the radiologist from Loqman Hakim and Al-Zahra Hospitals was 0.044 and 0.021, respectively.

Conclusions: The results of statistical tests and research variables show the efficiency of the CTC-CAD system created based on the hybrid of the ANN and particle swarm optimization compared to the opinion of radiologists in diagnosing colorectal polyps from CTC images.

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
International Journal of Preventive Medicine
International Journal of Preventive Medicine MEDICINE, GENERAL & INTERNAL-
CiteScore
3.20
自引率
4.80%
发文量
107
期刊介绍: International Journal of Preventive Medicine, a publication of Isfahan University of Medical Sciences, is a peer-reviewed online journal with Continuous print on demand compilation of issues published. The journal’s full text is available online at http://www.ijpvmjournal.net. The journal allows free access (Open Access) to its contents and permits authors to self-archive final accepted version of the articles on any OAI-compliant institutional / subject-based repository. The journal will cover technical and clinical studies related to health, ethical and social issues in field of Preventive Medicine. Articles with clinical interest and implications will be given preference.
期刊最新文献
A Case-control Study on the Association of Fruit and Vegetable Consumption with Risk of Breast Cancer. Comparison of Daily Dose of 400 and 600 Units of Vitamin D in the Prevention of Osteopenia of Prematurity in Infants with a Gestational Age of Less Than and Equal to 32 Weeks. Designing an Impact-Oriented Model of Research and Technology Evaluation: An Experience of I.R.Iran. Economic Burden of Hepatitis B at Different Stages of the Disease: A Systematic Review Study. Information Capsule: A New Approach for Summarizing Medical Information.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1