{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T18:23:27Z","timestamp":1787768607428,"version":"build-2784847793"},"reference-count":33,"publisher":"Ovid Technologies (Wolters Kluwer Health)","content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"abstract":"Abstract<\/jats:title>\n \n Background:<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Method:<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Results:<\/jats:title>\n \n The values of accuracy, sensitivity, and specificity of the model with a\n P<\/jats:italic>\n value = 0.000 as a result of the McNemar test were 0.9354, 0.9298, and 0.9889, respectively. Also, the result of the\n P<\/jats:italic>\n 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.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions:<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>","DOI":"10.4103\/ijpvm.ijpvm_373_22","type":"journal-article","created":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T12:00:33Z","timestamp":1706702433000},"update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":5,"title":["Designing a Hybrid Method of Artificial Neural Network and Particle Swarm Optimization to Diagnosis Polyps from Colorectal CT Images"],"prefix":"10.4103","volume":"15","author":[{"given":"Hossein Beigi","family":"Harchegani","sequence":"first","affiliation":[{"name":"Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamid","family":"Moghaddasi","sequence":"additional","affiliation":[{"name":"Professor of Health Information Management and Medical Informatics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2024,1]]},"reference":[{"key":"R1-20240830","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.media.2014.09.002","article-title":"How big is this neoplasia? live colonoscopic size measurement using the infocus-breakpoint","volume":"19","author":"Chadebecq","year":"2015","journal-title":"Med Image Anal"},{"key":"R2-20240830","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J Clin"},{"key":"R3-20240830","first-page":"52","article-title":"Judgement Comparison between Radiologists and Computer Aided Diagnosis Systems in Polyps Diagnosis based on Colorectal CT Images","volume":"17","author":"Harchegani","year":"2022","journal-title":"Journal of Knowledge &Health in Basic Medical Sciences"},{"key":"R4-20240830","doi-asserted-by":"crossref","first-page":"1635","DOI":"10.1109\/TBME.2006.877793","article-title":"An improved electronic colon cleansing method for detection of colonic polyps by virtual colonoscopy","volume":"53","author":"Wang","year":"2006","journal-title":"IEEE Trans Biomed Eng"},{"key":"R5-20240830","doi-asserted-by":"crossref","first-page":"1170","DOI":"10.1109\/TMI.2015.2482920","article-title":"Improving computer-aided detection using convolutional neural networks and random view aggregation","volume":"35","author":"Roth","year":"2016","journal-title":"IEEE Trans Med Imaging"},{"key":"R6-20240830","first-page":"165","article-title":"3D virtual colonoscopy with real-time volume rendering. 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