{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T20:07:25Z","timestamp":1743451645174},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,8,12]],"date-time":"2023-08-12T00:00:00Z","timestamp":1691798400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,12]],"date-time":"2023-08-12T00:00:00Z","timestamp":1691798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Comput"],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1007\/s12559-023-10180-1","type":"journal-article","created":{"date-parts":[[2023,8,12]],"date-time":"2023-08-12T15:01:42Z","timestamp":1691852502000},"page":"2175-2188","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An Improved Grey Wolf Optimization\u2013Based Convolutional Neural Network for the Segmentation of COVID-19 Lungs\u2013Infected Parts"],"prefix":"10.1007","volume":"15","author":[{"given":"P.","family":"Sridhar","sequence":"first","affiliation":[]},{"given":"Jayaraj","family":"Ramasamy","sequence":"additional","affiliation":[]},{"given":"Ravi","family":"Kumar","sequence":"additional","affiliation":[]},{"given":"Ramakrishnan","family":"Ramanathan","sequence":"additional","affiliation":[]},{"given":"Rakesh","family":"Nayak","sequence":"additional","affiliation":[]},{"given":"M.","family":"Tholkapiyan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,12]]},"reference":[{"issue":"8","key":"10180_CR1","doi-asserted-by":"publisher","first-page":"760","DOI":"10.1056\/NEJMe2001126","volume":"382","author":"S Perlman","year":"2020","unstructured":"Perlman S. Another decade, another coronavirus. N Engl J Med. 2020;382(8):760\u20132.","journal-title":"N Engl J Med"},{"issue":"7","key":"10180_CR2","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1002\/jmv.25766","volume":"92","author":"F He","year":"2020","unstructured":"He F, Deng Y, Li W. Coronavirus disease 2019: what we know? J Med Virol. 2020;92(7):719\u201325.","journal-title":"J Med Virol"},{"key":"10180_CR3","unstructured":"World Health Organization. Coronavirus disease 2019 (COVID-19): situation report. 2020;73."},{"issue":"2","key":"10180_CR4","doi-asserted-by":"publisher","first-page":"E15","DOI":"10.1148\/radiol.2020200490","volume":"296","author":"ZY Zu","year":"2020","unstructured":"Zu ZY, Jiang MD, Xu PP, Chen W, Ni QQ, Lu GM, Zhang LJ. Coronavirus disease 2019 (COVID-19): a perspective from China. Radiology. 2020;296(2):E15-25.","journal-title":"Radiology"},{"key":"10180_CR5","doi-asserted-by":"crossref","unstructured":"Allam Z, Dey G, Jones DS. Artificial intelligence (AI) provided early detection of the coronavirus (COVID-19) in China and will influence future urban health policy internationally.\u00a0Ai. 2020;1(2):156\u201365.","DOI":"10.3390\/ai1020009"},{"key":"10180_CR6","doi-asserted-by":"crossref","unstructured":"Pham QV, Nguyen DC, Huynh-The T, Hwang WJ, Pathirana PN. Artificial intelligence (AI) and big data for coronavirus (COVID-19) pandemic: a survey on the state-of-the-arts. IEEE Access. 2020;8:130820.","DOI":"10.1109\/ACCESS.2020.3009328"},{"key":"10180_CR7","unstructured":"Gozes O, Frid-Adar M, Sagie N, Zhang H, Ji W, Greenspan H. Coronavirus detection and analysis on chest ct with deep learning. 2020. arXiv preprint:\u00a0http:\/\/arxiv.org\/abs\/2004.02640."},{"key":"10180_CR8","unstructured":"Ghoshal B, Tucker A. Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection. 2020.\u00a0arXiv preprint: http:\/\/arxiv.org\/abs\/2003.10769."},{"issue":"1","key":"10180_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12880-020-00529-5","volume":"21","author":"A Saood","year":"2021","unstructured":"Saood A, Hatem I. COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet. BMC Med Imaging. 2021;21(1):1\u201310.","journal-title":"BMC Med Imaging"},{"key":"10180_CR10","doi-asserted-by":"crossref","unstructured":"Mahdy LN, Ezzat KA, Elmousalami HH, Ella HA, Hassanien AE. Automatic x-ray covid-19 lung image classification system based on multi-level thresholding and support vector machine. 2020.\u00a0MedRxiv.","DOI":"10.1101\/2020.03.30.20047787"},{"key":"10180_CR11","unstructured":"Yan Q, Wang B, Gong D, Luo C, Zhao W, Shen J, Shi Q, Jin S, Zhang L, You Z. COVID-19 chest CT image segmentation--a deep convolutional neural network solution. 2020. arXiv preprint: http:\/\/arxiv.org\/abs\/2004.10987."},{"key":"10180_CR12","doi-asserted-by":"crossref","unstructured":"Ranjbarzadeh R, Jafarzadeh Ghoushchi S, Bendechache M, Amirabadi A, Ab Rahman MN, Baseri Saadi S, Aghamohammadi A, Kooshki Forooshani M. Lung infection segmentation for COVID-19 pneumonia based on a cascade convolutional network from CT images.\u00a0BioMed Res Int.\u00a02021.","DOI":"10.1155\/2021\/5544742"},{"issue":"03","key":"10180_CR13","doi-asserted-by":"publisher","first-page":"2151004","DOI":"10.1142\/S0218001421510046","volume":"35","author":"D Singh","year":"2021","unstructured":"Singh D, Kumar V, Yadav V, Kaur M. Deep neural network-based screening model for COVID-19-infected patients using chest X-ray images. Int J Pattern Recognit Artif Intell. 2021;35(03):2151004.","journal-title":"Int J Pattern Recognit Artif Intell"},{"issue":"9","key":"10180_CR14","doi-asserted-by":"publisher","first-page":"6480","DOI":"10.1109\/TII.2021.3057524","volume":"17","author":"A Castiglione","year":"2021","unstructured":"Castiglione A, Vijayakumar P, Nappi M, Sadiq S, Umer M. Covid-19: Automatic detection of the novel coronavirus disease from ct images using an optimized convolutional neural network. IEEE Trans Industr Inf. 2021;17(9):6480\u20138.","journal-title":"IEEE Trans Industr Inf"},{"issue":"21","key":"10180_CR15","doi-asserted-by":"publisher","first-page":"16072","DOI":"10.1109\/JIOT.2021.3070306","volume":"8","author":"A Castiglione","year":"2021","unstructured":"Castiglione A, Umer M, Sadiq S, Obaidat MS, Vijayakumar P. The role of internet of things to control the outbreak of COVID-19 pandemic. IEEE Internet Things J. 2021;8(21):16072\u201382.","journal-title":"IEEE Internet Things J"},{"issue":"10","key":"10180_CR16","first-page":"3346","volume":"68","author":"S Vahdat","year":"2021","unstructured":"Vahdat S, Kamal M, Afzali-Kusha A, Pedram M. LATIM: loading-aware offline training method for inverter-based memristive neural networks. IEEE Trans Circuits Syst II Express Briefs. 2021;68(10):3346\u201350.","journal-title":"IEEE Trans Circuits Syst II Express Briefs"},{"key":"10180_CR17","doi-asserted-by":"publisher","first-page":"13814","DOI":"10.1109\/ACCESS.2021.3050193","volume":"9","author":"MS Kaiser","year":"2021","unstructured":"Kaiser MS, Mahmud M, Noor MBT, Zenia NZ, Al Mamun S, Mahmud KA, Azad S, Aradhya VM, Stephan P, Stephan T, Kannan R. iWorkSafe: towards healthy workplaces during COVID-19 with an intelligent pHealth App for industrial settings. Ieee Access. 2021;9:13814\u201328.","journal-title":"Ieee Access"},{"key":"10180_CR18","doi-asserted-by":"crossref","unstructured":"Aradhya VM, Mahmud M, Chowdhury M, Guru DS, Kaiser MS, Azad S. Learning through one shot: a phase by phase approach for COVID-19 chest X-ray classification. In\u00a02020 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES) IEEE. 2021;241\u201344.","DOI":"10.1109\/IECBES48179.2021.9398761"},{"issue":"9","key":"10180_CR19","doi-asserted-by":"publisher","first-page":"6489","DOI":"10.1109\/TII.2020.3048391","volume":"17","author":"T Mahmud","year":"2020","unstructured":"Mahmud T, Alam MJ, Chowdhury S, Ali SN, Rahman MM, Fattah SA, Saquib M. CovTANet: a hybrid tri-level attention-based network for lesion segmentation, diagnosis, and severity prediction of COVID-19 chest CT scans. IEEE Trans Industr Inf. 2020;17(9):6489\u201398.","journal-title":"IEEE Trans Industr Inf"},{"issue":"3","key":"10180_CR20","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1109\/TAI.2021.3064913","volume":"2","author":"T Mahmud","year":"2021","unstructured":"Mahmud T, Rahman MA, Fattah SA, Kung SY. CovSegNet: a multi encoder\u2013decoder architecture for improved lesion segmentation of COVID-19 chest CT scans. IEEE Trans Artif Intell. 2021;2(3):283\u201397.","journal-title":"IEEE Trans Artif Intell"},{"issue":"1","key":"10180_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-021-00874-4","volume":"3","author":"O Elharrouss","year":"2022","unstructured":"Elharrouss O, Subramanian N, Al-Maadeed S. An encoder\u2013decoder-based method for segmentation of COVID-19 lung infection in CT images. SN Comput Sci. 2022;3(1):1\u201312.","journal-title":"SN Comput Sci"},{"issue":"8","key":"10180_CR22","doi-asserted-by":"publisher","first-page":"2626","DOI":"10.1109\/TMI.2020.2996645","volume":"39","author":"DP Fan","year":"2020","unstructured":"Fan DP, Zhou T, Ji GP, Zhou Y, Chen G, Fu H, Shen J, Shao L. Inf-net: automatic covid-19 lung infection segmentation from ct images. IEEE Trans Med Imaging. 2020;39(8):2626\u201337.","journal-title":"IEEE Trans Med Imaging"},{"issue":"6","key":"10180_CR23","doi-asserted-by":"publisher","first-page":"N141","DOI":"10.1088\/0031-9155\/55\/6\/N01","volume":"55","author":"AV Chvetsov","year":"2010","unstructured":"Chvetsov AV, Paige SL. The influence of CT image noise on proton range calculation in radiotherapy planning. Phys Med Biol. 2010;55(6):N141.","journal-title":"Phys Med Biol"},{"key":"10180_CR24","doi-asserted-by":"crossref","unstructured":"Patro S, Sahu KK. Normalization: a preprocessing stage. 2015. arXiv preprint:http:\/\/arxiv.org\/abs\/1503.06462.","DOI":"10.17148\/IARJSET.2015.2305"},{"key":"10180_CR25","doi-asserted-by":"crossref","unstructured":"Gon\u00e7alves DN, de Moares Weber VA, Pistori JGB, da Costa Gomes R, de Araujo AV, Pereira MF, Gon\u00e7alves WN, Pistori H. Carcass image segmentation using CNN-based methods.\u00a0Inf Process Agric. 2020.","DOI":"10.1016\/j.inpa.2020.11.004"},{"key":"10180_CR26","doi-asserted-by":"crossref","unstructured":"Tian Z, Shen C, Wang X, Chen H. Boxinst: high-performance instance segmentation with box annotations. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2021;5443\u201352.","DOI":"10.1109\/CVPR46437.2021.00540"},{"issue":"11","key":"10180_CR27","doi-asserted-by":"publisher","first-page":"2645","DOI":"10.3390\/s19112645","volume":"19","author":"M Maqsood","year":"2019","unstructured":"Maqsood M, Nazir F, Khan U, Aadil F, Jamal H, Mehmood I, Song OY. Transfer learning assisted classification and detection of Alzheimer\u2019s disease stages using 3D MRI scans. Sensors. 2019;19(11):2645.","journal-title":"Sensors"},{"key":"10180_CR28","doi-asserted-by":"publisher","first-page":"198403","DOI":"10.1109\/ACCESS.2020.3035345","volume":"8","author":"S Albahli","year":"2020","unstructured":"Albahli S, Nida N, Irtaza A, Yousaf MH, Mahmood MT. Melanoma lesion detection and segmentation using YOLOv4-DarkNet and active contour. IEEE Access. 2020;8:198403\u201314.","journal-title":"IEEE Access"},{"key":"10180_CR29","doi-asserted-by":"crossref","unstructured":"Qassim H, Verma A, Feinzimer D. Compressed residual-VGG16 CNN model for big data places image recognition. In\u00a02018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC).\u00a0IEEE. 2018;169\u201375.","DOI":"10.1109\/CCWC.2018.8301729"},{"key":"10180_CR30","doi-asserted-by":"crossref","unstructured":"Carvalho T, De Rezende ER, Alves MT, Balieiro FK, Sovat RB. Exposing computer generated images by eye\u2019s region classification via transfer learning of VGG19 CNN. In\u00a02017 16th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE. 2017;866\u201370.","DOI":"10.1109\/ICMLA.2017.00-47"},{"issue":"2","key":"10180_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-020-0114-9","volume":"1","author":"D Theckedath","year":"2020","unstructured":"Theckedath D, Sedamkar RR. Detecting affect states using VGG16, ResNet50 and SE-ResNet50 networks. SN Comput Sci. 2020;1(2):1\u20137.","journal-title":"SN Comput Sci"},{"key":"10180_CR32","doi-asserted-by":"crossref","unstructured":"Ketkar N. Stochastic gradient descent. In\u00a0Deep Learning with Python. Apress Berkeley CA. 2017;113\u201332.","DOI":"10.1007\/978-1-4842-2766-4_8"},{"key":"10180_CR33","doi-asserted-by":"crossref","unstructured":"Nadimi-Shahraki MH, Taghian S, Mirjalili S. An improved grey wolf optimizer for solving engineering problems. Expert Syst Appl. 2021;166:113917.","DOI":"10.1016\/j.eswa.2020.113917"},{"key":"10180_CR34","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.swevo.2018.01.001","volume":"44","author":"S Gupta","year":"2019","unstructured":"Gupta S, Deep K. A novel random walk grey wolf optimizer. Swarm Evol Comput. 2019;44:101\u201312.","journal-title":"Swarm Evol Comput"},{"issue":"21","key":"10180_CR35","doi-asserted-by":"publisher","first-page":"7116","DOI":"10.3390\/s21217116","volume":"21","author":"LO Teixeira","year":"2021","unstructured":"Teixeira LO, Pereira RM, Bertolini D, Oliveira LS, Nanni L, Cavalcanti GD, Costa YM. Impact of lung segmentation on the diagnosis and explanation of COVID-19 in chest X-ray images. Sensors. 2021;21(21):7116.","journal-title":"Sensors"},{"key":"10180_CR36","doi-asserted-by":"crossref","unstructured":"Chen C, Xiao R, Zhang T, Lu Y, Guo X, Wang J, Chen H, Wang Z. Pathological lung segmentation in chest CT images based on improved random walker. Comput Methods Programs Biomed. 2021;200:105864.","DOI":"10.1016\/j.cmpb.2020.105864"},{"issue":"10","key":"10180_CR37","doi-asserted-by":"publisher","first-page":"2808","DOI":"10.1109\/TMI.2021.3066161","volume":"40","author":"Q Yao","year":"2021","unstructured":"Yao Q, Xiao L, Liu P, Zhou SK. Label-free segmentation of COVID-19 lesions in lung CT. IEEE Trans Med Imaging. 2021;40(10):2808\u201319.","journal-title":"IEEE Trans Med Imaging"},{"key":"10180_CR38","doi-asserted-by":"crossref","unstructured":"Saeedizadeh N, Minaee S, Kafieh R, Yazdani S, Sonka M. COVID TV-Unet: segmenting COVID-19 chest CT images using connectivity imposed Unet. Computer Methods and Programs in Biomedicine Update. 2021;1:100007.","DOI":"10.1016\/j.cmpbup.2021.100007"},{"key":"10180_CR39","unstructured":"Gozes O, Frid-Adar M, Greenspan H, Browning PD, Zhang H, Ji W, Bernheim A, Siegel E. Rapid ai development cycle for the coronavirus (covid-19) pandemic: initial results for automated detection & patient monitoring using deep learning ct image analysis. 2020. arXiv preprint:\u00a0http:\/\/arxiv.org\/abs\/2003.05037."}],"container-title":["Cognitive Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-023-10180-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12559-023-10180-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-023-10180-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T10:25:16Z","timestamp":1699871116000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12559-023-10180-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,12]]},"references-count":39,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["10180"],"URL":"https:\/\/doi.org\/10.1007\/s12559-023-10180-1","relation":{},"ISSN":["1866-9956","1866-9964"],"issn-type":[{"value":"1866-9956","type":"print"},{"value":"1866-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,12]]},"assertion":[{"value":"24 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Informed consent does not apply as this was a retrospective review with no identifying patient information.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"This article does not contain any studies with human or animal subjects performed by any of the authors.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and Animal Rights"}}]}}