{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T08:21:22Z","timestamp":1774945282326,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,3,2]],"date-time":"2021-03-02T00:00:00Z","timestamp":1614643200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,3,2]],"date-time":"2021-03-02T00:00:00Z","timestamp":1614643200000},"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":["J Ambient Intell Human Comput"],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1007\/s12652-021-02967-7","type":"journal-article","created":{"date-parts":[[2021,3,2]],"date-time":"2021-03-02T17:02:47Z","timestamp":1614704567000},"page":"2025-2043","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["COVID-19 classification using deep feature concatenation technique"],"prefix":"10.1007","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1860-4537","authenticated-orcid":false,"given":"Waleed","family":"Saad","sequence":"first","affiliation":[]},{"given":"Wafaa A.","family":"Shalaby","sequence":"additional","affiliation":[]},{"given":"Mona","family":"Shokair","sequence":"additional","affiliation":[]},{"given":"Fathi Abd","family":"El-Samie","sequence":"additional","affiliation":[]},{"given":"Moawad","family":"Dessouky","sequence":"additional","affiliation":[]},{"given":"Essam","family":"Abdellatef","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,2]]},"reference":[{"issue":"4","key":"2967_CR1","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1504\/IJHPCN.2018.096726","volume":"12","author":"F Amato","year":"2018","unstructured":"Amato F, Cozzolino G, Mazzeo A, Romano S (2018) Intelligent medical record management: a diagnosis support system. Int J High Perform Comput Netw 12(4):391\u2013399","journal-title":"Int J High Perform Comput Netw"},{"issue":"4","key":"2967_CR2","doi-asserted-by":"publisher","first-page":"e192561","DOI":"10.1001\/jamanetworkopen.2019.2561","volume":"2","author":"N Braman","year":"2019","unstructured":"Braman N, Prasanna P, Whitney J, Singh S, Beig N, Etesami M, Bates DD, Gallagher K, Bloch BN, Vulchi M et al (2019) Association of peritumoral radiomics with tumor biology and pathologic response to preoperative targeted therapy for her2 (erbb2)-positive breast cancer. JAMA Netw Open 2(4):e192561\u2013e192561","journal-title":"JAMA Netw Open"},{"key":"2967_CR3","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1109\/ACCESS.2014.2325029","volume":"2","author":"XW Chen","year":"2014","unstructured":"Chen XW, Lin X (2014) Big data deep learning: challenges and perspectives. IEEE Access 2:514\u2013525","journal-title":"IEEE Access"},{"key":"2967_CR4","doi-asserted-by":"crossref","unstructured":"Chen T, ChefdHotel C (2014) Deep learning based automatic immune cell detection for immunohistochemistry images. In: International workshop on machine learning in medical imaging, Springer, pp 17\u201324","DOI":"10.1007\/978-3-319-10581-9_3"},{"issue":"1","key":"2967_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-016-0001-8","volume":"6","author":"JZ Cheng","year":"2016","unstructured":"Cheng JZ, Ni D, Chou YH, Qin J, Tiu CM, Chang YC, Huang CS, Shen D, Chen CM (2016) Computer-aided diagnosis with deep learning architecture: applications to breast lesions in us images and pulmonary nodules in ct scans. Sci Rep 6(1):1\u201313","journal-title":"Sci Rep"},{"issue":"1","key":"2967_CR6","first-page":"154","volume":"8","author":"O Dorgham","year":"2018","unstructured":"Dorgham O, Al-Rahamneh B, Almomani A, Khatatneh KF et al (2018) Enhancing the security of exchanging and storing dicom medical images on the cloud. Int J Cloud Appl Comput (IJCAC) 8(1):154\u2013172","journal-title":"Int J Cloud Appl Comput (IJCAC)"},{"key":"2967_CR7","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.patrec.2019.11.015","volume":"129","author":"C Du","year":"2020","unstructured":"Du C, Wang Y, Wang C, Shi C, Xiao B (2020) Selective feature connection mechanism: concatenating multi-layer cnn features with a feature selector. Pattern Recogn Lett 129:108\u2013114","journal-title":"Pattern Recogn Lett"},{"key":"2967_CR8","doi-asserted-by":"publisher","unstructured":"El-Shafai W, El-Samie FA (2020) Going deeper with convolutions. In: Extensive and Augmented COVID-19 X-Ray and CT Chest Images Dataset. https:\/\/doi.org\/10.17632\/8h65ywd2jr.2","DOI":"10.17632\/8h65ywd2jr.2"},{"key":"2967_CR9","doi-asserted-by":"crossref","unstructured":"Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115\u2013118","DOI":"10.1038\/nature21056"},{"key":"2967_CR10","unstructured":"Farooq M, Hafeez A (2020) Covid-resnet: a deep learning framework for screening of covid19 from radiographs. arXiv preprint arXiv:200314395"},{"issue":"4","key":"2967_CR11","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1109\/MCOM.2018.1700817","volume":"56","author":"A Ghoneim","year":"2018","unstructured":"Ghoneim A, Muhammad G, Amin SU, Gupta B (2018) Medical image forgery detection for smart healthcare. IEEE Commun Mag 56(4):33\u201337","journal-title":"IEEE Commun Mag"},{"issue":"2","key":"2967_CR12","doi-asserted-by":"publisher","first-page":"36","DOI":"10.4018\/IJSSCI.2018040103","volume":"10","author":"P Guo","year":"2018","unstructured":"Guo P, Evans A, Bhattacharya P (2018) Nuclei segmentation for quantification of brain tumors in digital pathology images. Int J Softw Sci Comput Intell (IJSSCI) 10(2):36\u201349","journal-title":"Int J Softw Sci Comput Intell (IJSSCI)"},{"key":"2967_CR13","doi-asserted-by":"crossref","unstructured":"Hall LO, Paul R, Goldgof DB, Goldgof GM (2020) Finding covid-19 from chest x-rays using deep learning on a small dataset. arXiv preprint arXiv:200402060","DOI":"10.36227\/techrxiv.12083964.v2"},{"key":"2967_CR14","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"10223","key":"2967_CR15","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1016\/S0140-6736(20)30183-5","volume":"395","author":"C Huang","year":"2020","unstructured":"Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X et al (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet 395(10223):497\u2013506","journal-title":"The Lancet"},{"key":"2967_CR16","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"2967_CR17","unstructured":"Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) Squeezenet: alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size. arXiv preprint arXiv:160207360"},{"key":"2967_CR18","doi-asserted-by":"crossref","unstructured":"Jin S, Wang B, Xu H, Luo C, Wei L, Zhao W, Hou X, Ma W, Xu Z, Zheng Z, et\u00a0al. (2020) Ai-assisted ct imaging analysis for covid-19 screening: building and deploying a medical ai system in four weeks. MedRxiv","DOI":"10.1101\/2020.03.19.20039354"},{"key":"2967_CR19","doi-asserted-by":"crossref","unstructured":"Jmour N, Zayen S, Abdelkrim A (2018a) Convolutional neural networks for image classification. In: 2018 International Conference on Advanced Systems and Electric Technologies (IC\\_ASET), IEEE, pp 397\u2013402","DOI":"10.1109\/ASET.2018.8379889"},{"key":"2967_CR20","doi-asserted-by":"crossref","unstructured":"Jmour N, Zayen S, Abdelkrim A (2018b) Convolutional neural networks for image classification. In: 2018 International Conference on Advanced Systems and Electric Technologies (IC\\_ASET), IEEE, pp 397\u2013402","DOI":"10.1109\/ASET.2018.8379889"},{"key":"2967_CR21","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inform Process Syst 25:1097\u20131105","journal-title":"Adv Neural Inform Process Syst"},{"key":"2967_CR22","unstructured":"LeCun Y, et\u00a0al. (2015) Lenet-5, convolutional neural networks 20(5):14 http:\/\/yann.lecun.com\/exdb\/lenet"},{"issue":"6","key":"2967_CR23","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1109\/34.216733","volume":"15","author":"F Leymarie","year":"1993","unstructured":"Leymarie F, Levine MD (1993) Tracking deformable objects in the plane using an active contour model. IEEE Trans Pattern Anal Mach Intell 15(6):617\u2013634","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"16","key":"2967_CR24","doi-asserted-by":"publisher","first-page":"7107","DOI":"10.1109\/JSEN.2019.2913281","volume":"19","author":"X Liang","year":"2019","unstructured":"Liang X, Hu P, Zhang L, Sun J, Yin G (2019) Mcfnet: multi-layer concatenation fusion network for medical images fusion. IEEE Sens J 19(16):7107\u20137119","journal-title":"IEEE Sens J"},{"key":"2967_CR25","unstructured":"Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, Bai J, Lu Y, Fang Z, Song Q et al (2020) Artificial intelligence distinguishes covid-19 from community acquired pneumonia on chest ct. Radiology"},{"key":"2967_CR26","doi-asserted-by":"publisher","first-page":"121685","DOI":"10.1109\/ACCESS.2019.2936215","volume":"7","author":"C Ma","year":"2019","unstructured":"Ma C, Mu X, Sha D (2019) Multi-layers feature fusion of convolutional neural network for scene classification of remote sensing. IEEE Access 7:121685\u2013121694","journal-title":"IEEE Access"},{"issue":"11","key":"2967_CR27","doi-asserted-by":"publisher","first-page":"2787","DOI":"10.1109\/TIP.2007.908073","volume":"16","author":"O Michailovich","year":"2007","unstructured":"Michailovich O, Rathi Y, Tannenbaum A (2007) Image segmentation using active contours driven by the bhattacharyya gradient flow. IEEE Trans Image Process 16(11):2787\u20132801","journal-title":"IEEE Trans Image Process"},{"issue":"1","key":"2967_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-014-0007-7","volume":"2","author":"MM Najafabadi","year":"2015","unstructured":"Najafabadi MM, Villanustre F, Khoshgoftaar TM, Seliya N, Wald R, Muharemagic E (2015) Deep learning applications and challenges in big data analytics. J Big Data 2(1):1\u201321","journal-title":"J Big Data"},{"key":"2967_CR29","doi-asserted-by":"crossref","unstructured":"Narin A, Kaya C, Pamuk Z (2020) Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. arXiv preprint arXiv:200310849","DOI":"10.1007\/s10044-021-00984-y"},{"key":"2967_CR30","doi-asserted-by":"crossref","unstructured":"Nguyen LD, Lin D, Lin Z, Cao J (2018) Deep cnns for microscopic image classification by exploiting transfer learning and feature concatenation. In: 2018 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE, pp 1\u20135","DOI":"10.1109\/ISCAS.2018.8351550"},{"key":"2967_CR31","doi-asserted-by":"publisher","first-page":"55135","DOI":"10.1109\/ACCESS.2020.2978629","volume":"8","author":"N Noreen","year":"2020","unstructured":"Noreen N, Palaniappan S, Qayyum A, Ahmad I, Imran M, Shoaib M (2020) A deep learning model based on concatenation approach for the diagnosis of brain tumor. IEEE Access 8:55135\u201355144","journal-title":"IEEE Access"},{"issue":"2","key":"2967_CR32","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1504\/IJHPCN.2019.10022723","volume":"14","author":"Z Qu","year":"2019","unstructured":"Qu Z, He H, Liu W, Ma S (2019) A self-adaptive quantum steganography algorithm based on qlsb modification in watermarked quantum image. Int J High Perform Comput Network 14(2):121\u2013129","journal-title":"Int J High Perform Comput Network"},{"key":"2967_CR33","doi-asserted-by":"publisher","first-page":"100360","DOI":"10.1016\/j.imu.2020.100360","volume":"19","author":"M Rahimzadeh","year":"2020","unstructured":"Rahimzadeh M, Attar A (2020) A modified deep convolutional neural network for detecting covid-19 and pneumonia from chest x-ray images based on the concatenation of xception and resnet50v2. Inform Med Unlock 19:100360","journal-title":"Inform Med Unlock"},{"key":"2967_CR34","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.jocs.2018.12.003","volume":"30","author":"M Sajjad","year":"2019","unstructured":"Sajjad M, Khan S, Muhammad K, Wu W, Ullah A, Baik SW (2019) Multi-grade brain tumor classification using deep cnn with extensive data augmentation. J Comput Sci 30:174\u2013182","journal-title":"J Comput Sci"},{"key":"2967_CR35","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"2967_CR36","doi-asserted-by":"publisher","first-page":"95","DOI":"10.3389\/fnins.2019.00095","volume":"13","author":"A Sengupta","year":"2019","unstructured":"Sengupta A, Ye Y, Wang R, Liu C, Roy K (2019) Going deeper in spiking neural networks: Vgg and residual architectures. Front Neurosci 13:95","journal-title":"Front Neurosci"},{"key":"2967_CR37","doi-asserted-by":"crossref","unstructured":"Shi F, Xia L, Shan F, Wu D, Wei Y, Yuan H, Jiang H, Gao Y, Sui H, Shen D (2020) Large-scale screening of covid-19 from community acquired pneumonia using infection size-aware classification. arXiv preprint arXiv:200309860","DOI":"10.1088\/1361-6560\/abe838"},{"issue":"4","key":"2967_CR38","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1007\/s12098-020-03263-6","volume":"87","author":"T Singhal","year":"2020","unstructured":"Singhal T (2020) A review of coronavirus disease-2019 (covid-19). Indian J Pediatr 87(4):281\u2013286","journal-title":"Indian J Pediatr"},{"key":"2967_CR39","unstructured":"Singh K, Gupta G, Vig L, Shroff G, Agarwal P (2017) Deep convolutional neural networks for pairwise causality. arXiv preprint arXiv:170100597"},{"key":"2967_CR40","doi-asserted-by":"crossref","unstructured":"Song Y, Zheng S, Li L, Zhang X, Zhang X, Huang Z, Chen J, Zhao H, Jie Y, Wang R, et\u00a0al. (2020) Deep learning enables accurate diagnosis of novel coronavirus (covid-19) with ct images. MedRxiv","DOI":"10.1109\/TCBB.2021.3065361"},{"key":"2967_CR41","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015a) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"2967_CR42","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015b) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"2967_CR43","unstructured":"Tan M, Le Q (2019) Efficientnet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, PMLR, pp 6105\u20136114"},{"issue":"2","key":"2967_CR44","doi-asserted-by":"publisher","first-page":"E41","DOI":"10.1148\/radiol.2020200343","volume":"296","author":"X Xie","year":"2020","unstructured":"Xie X, Zhong Z, Zhao W, Zheng C, Wang F, Liu J (2020a) Chest ct for typical coronavirus disease 2019 (covid-19) pneumonia: relationship to negative rt-pcr testing. Radiology 296(2):E41\u2013E45","journal-title":"Radiology"},{"issue":"2","key":"2967_CR45","doi-asserted-by":"publisher","first-page":"E41","DOI":"10.1148\/radiol.2020200343","volume":"296","author":"X Xie","year":"2020","unstructured":"Xie X, Zhong Z, Zhao W, Zheng C, Wang F, Liu J (2020b) Chest ct for typical coronavirus disease 2019 (covid-19) pneumonia: relationship to negative rt-pcr testing. Radiology 296(2):E41\u2013E45","journal-title":"Radiology"},{"issue":"10","key":"2967_CR46","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.eng.2020.04.010","volume":"6","author":"X Xu","year":"2020","unstructured":"Xu X, Jiang X, Ma C, Du P, Li X, Lv S, Yu L, Ni Q, Chen Y, Su J et al (2020) A deep learning system to screen novel coronavirus disease 2019 pneumonia. Engineering 6(10):1122\u20131129","journal-title":"Engineering"},{"key":"2967_CR47","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1109\/ACCESS.2017.2775038","volume":"6","author":"Q Zheng","year":"2017","unstructured":"Zheng Q, Wang X, Khan MK, Zhang W, Gupta BB, Guo W (2017) A lightweight authenticated encryption scheme based on chaotic scml for railway cloud service. IEEE Access 6:711\u2013722","journal-title":"IEEE Access"},{"key":"2967_CR48","doi-asserted-by":"crossref","unstructured":"Zheng C, Deng X, Fu Q, Zhou Q, Feng J, Ma H, Liu W, Wang X (2020) Deep learning-based detection for covid-19 from chest ct using weak label. MedRxiv","DOI":"10.1101\/2020.03.12.20027185"},{"key":"2967_CR49","doi-asserted-by":"crossref","unstructured":"Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, Zhao X, Huang B, Shi W, Lu R, et\u00a0al. (2020) A novel coronavirus from patients with pneumonia in china, 2019. N Engl J Med","DOI":"10.1056\/NEJMoa2001017"}],"container-title":["Journal of Ambient Intelligence and Humanized Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-021-02967-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12652-021-02967-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-021-02967-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T12:02:33Z","timestamp":1648209753000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12652-021-02967-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,2]]},"references-count":49,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["2967"],"URL":"https:\/\/doi.org\/10.1007\/s12652-021-02967-7","relation":{},"ISSN":["1868-5137","1868-5145"],"issn-type":[{"value":"1868-5137","type":"print"},{"value":"1868-5145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,2]]},"assertion":[{"value":"31 August 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 February 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 March 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}