{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T07:08:39Z","timestamp":1742972919255,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811929472"},{"type":"electronic","value":"9789811929489"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-19-2948-9_40","type":"book-chapter","created":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T06:03:51Z","timestamp":1662012231000},"page":"413-425","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Performance Comparison of Different Convolutional Neural Network Models for the Detection of COVID-19"],"prefix":"10.1007","author":[{"given":"S. V.","family":"Kogilavani","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R.","family":"Sandhiya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S.","family":"Malliga","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,2]]},"reference":[{"key":"40_CR1","doi-asserted-by":"crossref","unstructured":"Priya C et al (2021) Automatic optimized CNN based COVID-19 lung infection segmentation from CT image. Mater Today Proc","DOI":"10.1016\/j.matpr.2021.01.820"},{"key":"40_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107747","volume":"114","author":"A Oulefki","year":"2021","unstructured":"Oulefki A et al (2021) Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images. Pattern Recogn 114:107747","journal-title":"Pattern Recogn"},{"key":"40_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114883","volume":"176","author":"B Nigam","year":"2021","unstructured":"Nigam B et al (2021) COVID-19: automatic detection from X-ray images by utilizing deep learning methods. Expert Syst Appl 176:114883","journal-title":"Expert Syst Appl"},{"key":"40_CR4","doi-asserted-by":"crossref","unstructured":"Hassantabar S, Ahmadi M, Sharifi A (2020) Diagnosis and detection of infected tissue of COVID-19 patients based on lung X-ray image using convolutional neural network approaches. Chaos Solitons Fractals 140:110170","DOI":"10.1016\/j.chaos.2020.110170"},{"key":"40_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102750","volume":"68","author":"Z Nabizadeh-Shahre-Babak","year":"2021","unstructured":"Nabizadeh-Shahre-Babak Z et al (2021) Detection of COVID-19 in X-ray images by classification of bag of visual words using neural networks. Biomed Signal Process Control 68:102750","journal-title":"Biomed Signal Process Control"},{"key":"40_CR6","doi-asserted-by":"crossref","unstructured":"Turkoglu M (2021) COVID-19 detection system using chest CT images and multiple kernels-extreme learning machine based on deep neural network. IRBM","DOI":"10.1016\/j.irbm.2021.01.004"},{"key":"40_CR7","unstructured":"Majeed T et al (2020) Covid-19 detection using CNN transfer learning from X-ray images. medRxiv"},{"issue":"3","key":"40_CR8","doi-asserted-by":"publisher","first-page":"867","DOI":"10.1016\/j.bbe.2021.05.013","volume":"41","author":"SH Kassania","year":"2021","unstructured":"Kassania SH et al (2021) Automatic detection of coronavirus disease (COVID-19) in X-ray and CT images: a machine learning based approach. Biocybernetics Biomed Eng 41(3):867\u2013879","journal-title":"Biocybernetics Biomed Eng"},{"key":"40_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102365","volume":"64","author":"SR Nayak","year":"2021","unstructured":"Nayak SR et al (2021) Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: a comprehensive study. Biomed Signal Process Control 64:102365","journal-title":"Biomed Signal Process Control"},{"key":"40_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104306","volume":"132","author":"S Serte","year":"2021","unstructured":"Serte S, Demirel H (2021) Deep learning for diagnosis of COVID-19 using 3D CT scans. Comput Biol Med 132:104306","journal-title":"Comput Biol Med"},{"key":"40_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103795","volume":"121","author":"AA Ardakani","year":"2020","unstructured":"Ardakani AA et al (2020) Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: results of 10 convolutional neural networks. Comput Biol Med 121:103795","journal-title":"Comput Biol Med"},{"key":"40_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107645","volume":"110","author":"S Albahli","year":"2021","unstructured":"Albahli S, Ayub N, Shiraz M (2021) Coronavirus disease (COVID-19) detection using X-ray images and enhanced DenseNet. Appl Soft Comput 110:107645","journal-title":"Appl Soft Comput"},{"key":"40_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103792","volume":"121","author":"T Ozturk","year":"2020","unstructured":"Ozturk T et al (2020) Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 121:103792","journal-title":"Comput Biol Med"},{"key":"40_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102920","volume":"69","author":"S Thakur","year":"2021","unstructured":"Thakur S, Kumar A (2021) X-ray and CT-scan-based automated detection and classification of COVID-19 using convolutional neural networks (CNN). Biomed Signal Process Control 69:102920","journal-title":"Biomed Signal Process Control"},{"key":"40_CR15","doi-asserted-by":"crossref","unstructured":"Fouladi S et al (2021) Efficient deep neural networks for classification of COVID-19 based on CT images: virtualization via software defined radio. Comput Commun","DOI":"10.1016\/j.comcom.2021.06.011"},{"key":"40_CR16","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.patrec.2020.10.001","volume":"140","author":"M Polsinelli","year":"2020","unstructured":"Polsinelli M, Cinque L, Placidi G (2020) A light CNN for detecting COVID-19 from CT scans of the chest. Pattern Recogn Lett 140:95\u2013100","journal-title":"Pattern Recogn Lett"},{"key":"40_CR17","doi-asserted-by":"publisher","DOI":"10.34740\/KAGGLE\/DSV\/1199870","author":"E Soares","year":"2020","unstructured":"Soares E, Angelov P (2020) SARS-COV-2 Ct-scan dataset. Kaggle. https:\/\/doi.org\/10.34740\/KAGGLE\/DSV\/1199870","journal-title":"Kaggle"},{"issue":"2","key":"40_CR18","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1016\/j.bbe.2021.04.006","volume":"41","author":"NK Mishra","year":"2021","unstructured":"Mishra NK, Singh P, Joshi SD (2021) Automated detection of COVID-19 from CT scan using convolutional neural network. Biocybernetics Biomed Eng 41(2):572\u2013588","journal-title":"Biocybernetics Biomed Eng"},{"key":"40_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2021.103751","volume":"117","author":"O Shahid","year":"2021","unstructured":"Shahid O et al (2021) Machine learning research towards combating COVID-19: virus detection, spread prevention, and medical assistance. J Biomed Inform 117:103751","journal-title":"J Biomed Inform"},{"issue":"4","key":"40_CR20","doi-asserted-by":"publisher","first-page":"1436","DOI":"10.1016\/j.bbe.2020.08.005","volume":"40","author":"B Abraham","year":"2020","unstructured":"Abraham B, Nair MS (2020) Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier. Biocybernetics Biomed Eng 40(4):1436\u20131445","journal-title":"Biocybernetics Biomed Eng"}],"container-title":["Lecture Notes on Data Engineering and Communications Technologies","Proceedings of 7th International Conference on Harmony Search, Soft Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-2948-9_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T06:22:43Z","timestamp":1662013363000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-2948-9_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811929472","9789811929489"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-2948-9_40","relation":{},"ISSN":["2367-4512","2367-4520"],"issn-type":[{"type":"print","value":"2367-4512"},{"type":"electronic","value":"2367-4520"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"2 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}