{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T20:32:34Z","timestamp":1787776354782,"version":"build-2784847793"},"reference-count":13,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"4","content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,10]]},"abstract":"<jats:p>The latest World Health Organization statistics show that the number of people living with COVID-19 disease is now more than 42 million worldwide. Some diagnosis methods include detecting and observing clinical symptoms associated with the disease (fever, dry cough, shortness of breath, sore throat, and muscle fatigue). Some other methods, such as computed tomography (CT)-scan imaging from the lungs, are the more accurate diagnostic methods. In this study, we examine the types of abnormal COVID-19 can cause in the lungs of infected subjects and detect and classify this disease. In this paper, we used data from the lung's CT-scan images from the 79 participants. To do this, in this article, for processing CT-scan images of the lungs to diagnose and classification of the COVID-19 disease in men and women of different ages, for rapid diagnosis and high accuracy of this disease by the automatic classification algorithm is used. The final results showed that the proposed method could base on different categories (gender, age categories, and type of damage caused by COVID-19) with high detection and classification accuracy. The algorithm presented in this article has accurately identified the data of healthy subjects and patients with coronavirus.<\/jats:p>","DOI":"10.4103\/jmss.jmss_55_20","type":"journal-article","created":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T06:11:09Z","timestamp":1634796669000},"page":"274-284","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":4,"title":["Detection and Classification of COVID-19 by Lungs Computed Tomography Scan Image Processing using Intelligence Algorithm"],"prefix":"10.4103","volume":"11","author":[{"given":"Naser","family":"Safdarian","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nader Jafarnia","family":"Dabanloo","sequence":"additional","affiliation":[{"name":"Engineering Research Center in Medicine and Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2021,10,20]]},"reference":[{"key":"R1-20250528","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1007\/s12519-020-00345-5","article-title":"Diagnosis and treatment recommendations for pediatric respiratory infection caused by the 2019 novel coronavirus","volume":"16","author":"Chen","year":"2020","journal-title":"World J Pediatr"},{"key":"R2-20250528","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/S0140-6736(20)30183-5","article-title":"Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China","volume":"395","author":"Huang","year":"2020","journal-title":"Lancet"},{"key":"R3-20250528","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/S0140-6736(20)30154-9","article-title":"A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: A study of a family cluster","volume":"395","author":"Chan","year":"2020","journal-title":"The Lancet"},{"key":"R4-20250528","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1016\/j.jiph.2020.03.019","article-title":"Coronavirus disease 2019 (COVID-19): A literature review","volume":"13","author":"Harapan","year":"2020","journal-title":"J Infect Public Health"},{"key":"R5-20250528","doi-asserted-by":"crossref","first-page":"108972","DOI":"10.1016\/j.ejrad.2020.108972","article-title":"High-resolution computed tomography manifestations of COVID-19 infections in patients of different ages","volume":"126","author":"Chen","year":"2020","journal-title":"Eur J Radiol"},{"key":"R6-20250528","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1148\/radiol.2020200642","article-title":"Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: A report of 1014 cases","volume":"296","author":"Ai","year":"2020","journal-title":"Radiology"},{"key":"R7-20250528","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.ijid.2020.04.003","article-title":"High-resolution chest CT features and clinical characteristics of patients infected with COVID-19 in Jiangsu, China","volume":"95","author":"Dai","year":"2020","journal-title":"Int J Infect Dis"},{"key":"R8-20250528","doi-asserted-by":"crossref","first-page":"101794","DOI":"10.1016\/j.media.2020.101794","article-title":"Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning","volume":"65","author":"Minaee","year":"2020","journal-title":"Med Image Anal"},{"key":"R9-20250528","doi-asserted-by":"crossref","first-page":"110170","DOI":"10.1016\/j.chaos.2020.110170","article-title":"Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches","volume":"140","author":"Hassantabar","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"R10-20250528","doi-asserted-by":"crossref","first-page":"105581","DOI":"10.1016\/j.cmpb.2020.105581","article-title":"CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images","volume":"196","author":"Khan","year":"2020","journal-title":"Comput Methods Programs Biomed"},{"key":"R11-20250528","doi-asserted-by":"crossref","first-page":"109761","DOI":"10.1016\/j.mehy.2020.109761","article-title":"COVIDiagnosis-Net: Deep Bayes-SqueezeNet based Diagnostic of the coronavirus disease 2019 (COVID-19) from X-ray images","volume":"140","author":"Ucar","year":"2020","journal-title":"Med Hypotheses"},{"key":"R12-20250528","doi-asserted-by":"crossref","first-page":"110055","DOI":"10.1016\/j.chaos.2020.110055","article-title":"An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data","volume":"139","author":"Peng","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"R14-20250528","doi-asserted-by":"crossref","first-page":"100391","DOI":"10.1016\/j.imu.2020.100391","article-title":"Hybrid deep learning for detecting lung diseases from X-ray images","volume":"20","author":"Bharati","year":"2020","journal-title":"Inform Med Unlocked"}],"container-title":["Journal of Medical Signals &amp; Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ovid.com\/10.4103\/jmss.JMSS_55_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T20:08:13Z","timestamp":1787774893000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ovid.com\/10.4103\/jmss.JMSS_55_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10]]},"references-count":13,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021]]}},"URL":"https:\/\/doi.org\/10.4103\/jmss.jmss_55_20","relation":{},"ISSN":["2228-7477"],"issn-type":[{"value":"2228-7477","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10]]},"assertion":[{"value":"2020-07-24","name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-11-25","name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}}]}}