{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T15:24:16Z","timestamp":1780500256078,"version":"3.54.1"},"reference-count":99,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,5,24]],"date-time":"2022-05-24T00:00:00Z","timestamp":1653350400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,5,24]],"date-time":"2022-05-24T00:00:00Z","timestamp":1653350400000},"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":["Multimedia Systems"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s00530-022-00948-0","type":"journal-article","created":{"date-parts":[[2022,5,24]],"date-time":"2022-05-24T05:02:36Z","timestamp":1653368556000},"page":"1465-1479","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Deep learning in multimedia healthcare applications: a review"],"prefix":"10.1007","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4659-7693","authenticated-orcid":false,"suffix":"V","given":"Diana P.","family":"Tob\u00f3n","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. Shamim","family":"Hossain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ghulam","family":"Muhammad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Josu","family":"Bilbao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulmotaleb El","family":"Saddik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,5,24]]},"reference":[{"key":"948_CR1","unstructured":"W. H. Organization, \u201cWorld Health Organization,\u201d [Online]. Available: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/noncommunicable-diseases. [Accessed 4 December 2020]."},{"issue":"21","key":"948_CR2","doi-asserted-by":"publisher","first-page":"2616","DOI":"10.1001\/jama.291.21.2616","volume":"291","author":"D Yach","year":"2004","unstructured":"Yach, D., Hawkes, C., Gould, C.L., Hofman, K.J.: The global burden of chronic diseases: overcoming impediments to prevention and control. J. Amer. Med. Assoc. 291(21), 2616\u20132622 (2004)","journal-title":"J. Amer. Med. Assoc."},{"key":"948_CR3","unstructured":"W. H. Organization, \u201cWorld Health Organization,\u201d [Online]. Available: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/ageing-and-health. [Accessed 4 December 2020]."},{"key":"948_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/s00530-021-00839-w","author":"Q KashifNaseer","year":"2021","unstructured":"KashifNaseer, Q., et al.: Self-assessment  and deep learning-based coronavirus detection and medical diagnosis systems for healthcare. Multimed Syst (2021). https:\/\/doi.org\/10.1007\/s00530-021-00839-w","journal-title":"Multimed Syst"},{"key":"948_CR5","unstructured":"W. H. Organization, \"World Health Organization,\" [Online]. Available: https:\/\/www.who.int\/emergencies\/diseases\/novel-coronavirus-2019. [Accessed 8 December 2020]."},{"key":"948_CR6","doi-asserted-by":"publisher","first-page":"41034","DOI":"10.1109\/ACCESS.2018.2856238","volume":"6","author":"M Alhussein","year":"2018","unstructured":"Alhussein, M., Muhammad, G.: Voice pathology detection using deep learning on mobile healthcare framework. IEEE Access 6, 41034\u201341041 (2018)","journal-title":"IEEE Access"},{"key":"948_CR7","doi-asserted-by":"publisher","first-page":"46474","DOI":"10.1109\/ACCESS.2019.2905597","volume":"7","author":"M Alhussein","year":"2019","unstructured":"Alhussein, M., Muhammad, G.: Automatic voice pathology monitoring using parallel deep models for smart healthcare. IEEE Access 7, 46474\u201346479 (2019)","journal-title":"IEEE Access"},{"issue":"4","key":"948_CR8","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MWC.2013.6590048","volume":"20","author":"DP Tob\u00f3n","year":"2013","unstructured":"Tob\u00f3n, D.P., Falk, T.H., Maier, M.: Context awareness in WBANs: a survey on medical and non-medical applications. IEEE Wirel. Commun. 20(4), 30\u201337 (2013)","journal-title":"IEEE Wirel. Commun."},{"key":"948_CR9","doi-asserted-by":"publisher","first-page":"3107","DOI":"10.1007\/s11042-020-08896-5","volume":"81","author":"Y Dai","year":"2022","unstructured":"Dai, Y., Wang, G., Muhammad, K., Liu, S.: A closed-loop healthcare processing approach based on deep reinforcement learning. Multimed. Tools Appl. 81, 3107\u20133129 (2022)","journal-title":"Multimed. Tools Appl."},{"key":"948_CR10","doi-asserted-by":"crossref","unstructured":"Anwer, DN., Ozbay, S.: \u201cLung Cancer Classification and Detection Using Convolutional Neural Networks.\u201d Proceedings of the 6th International Conference on Engineering & MIS. (2020)","DOI":"10.1145\/3410352.3410822"},{"issue":"4","key":"948_CR11","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1097\/MAJ.0b013e3182896cee","volume":"345","author":"JC Sieverdes","year":"2013","unstructured":"Sieverdes, J.C., Treiber, F., Jenkins, C., Hermayer, K.: Improving diabetes management with mobile health technology. Am. J. Med. Sci. 345(4), 289\u2013295 (2013)","journal-title":"Am. J. Med. Sci."},{"issue":"11","key":"948_CR12","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.2588","volume":"15","author":"M Kirwan","year":"2013","unstructured":"Kirwan, M., Vandelanotte, C., Fenning, A., Duncan, M.J.: Diabetes self-management smartphone application for adults with type 1 diabetes: randomized controlled trial. J. Med. Internet Res. 15(11), e235 (2013)","journal-title":"J. Med. Internet Res."},{"issue":"6","key":"948_CR13","doi-asserted-by":"publisher","first-page":"1079","DOI":"10.1109\/TITB.2012.2206116","volume":"16","author":"HR Maamar","year":"2012","unstructured":"Maamar, H.R., Boukerche, A., Petriu, E.M.: 3-D streaming supplying partner protocols for mobile collaborative exergaming for health. IEEE Trans. Inf. Technol. Biomed. 16(6), 1079\u20131095 (2012)","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"issue":"1","key":"948_CR14","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1109\/JSYST.2015.2460747","volume":"11","author":"Y Zhang","year":"2017","unstructured":"Zhang, Y., Qiu, M., Tsai, C.W., Hassan, M.M., Alamri, A.: Health-CPS: healthcare cyber-physical system assisted by cloud and big data. IEEE Syst. J. 11(1), 88\u201395 (2017)","journal-title":"IEEE Syst. J."},{"issue":"2","key":"948_CR15","doi-asserted-by":"publisher","first-page":"e15","DOI":"10.2196\/mhealth.2737","volume":"1","author":"B Mart\u00ednez-P\u00e9rez","year":"2013","unstructured":"Mart\u00ednez-P\u00e9rez, B., de la la TorreD\u00edez, I., L\u00f3pez-Coronado, M., Herreros-Gonz\u00e1lez, J.: Mobile apps in cardiology: review. JMIR Mhealth Uhealth 1(2), e15 (2013)","journal-title":"JMIR Mhealth Uhealth"},{"issue":"11","key":"948_CR16","doi-asserted-by":"publisher","first-page":"1753","DOI":"10.1002\/dac.2778","volume":"28","author":"I Bisio","year":"2014","unstructured":"Bisio, I., Lavagetto, F., Marchese, M., Sciarrone, A.: A smartphone centric platform for remote health monitoring of heart failure. Int. J. Commun. Syst. 28(11), 1753\u20131771 (2014)","journal-title":"Int. J. Commun. Syst."},{"issue":"2","key":"948_CR17","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1109\/TITB.2009.2037616","volume":"14","author":"J Fayn","year":"2010","unstructured":"Fayn, J., Rubel, P.: Toward a personal health society in cardiology. IEEE Trans. Inf. Technol. Biomed. 14(2), 401\u2013409 (2010)","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"issue":"9","key":"948_CR18","doi-asserted-by":"publisher","first-page":"e197","DOI":"10.2196\/jmir.2529","volume":"15","author":"J Fontecha","year":"2013","unstructured":"Fontecha, J., Herv\u00e1s, R., Bravo, J., Navarro, J.F.: A mobile and ubiquitous approach for supporting frailty assessment in elderly people. J. Med. Internet. Res. 15(9), e197 (2013)","journal-title":"J. Med. Internet. Res."},{"issue":"9","key":"948_CR19","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1109\/JSAC.2013.SUP.0513001","volume":"31","author":"G Chiarini","year":"2013","unstructured":"Chiarini, G., Ray, P., Akter, S., Masella, C., Ganz, A.: mhealth technologies for chronic diseases and elders: a systematic review. IEEE J. Sel. Areas Commun. 31(9), 6\u201318 (2013)","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"948_CR20","doi-asserted-by":"publisher","first-page":"52277","DOI":"10.1109\/ACCESS.2018.2869790","volume":"6","author":"Y Gao","year":"2018","unstructured":"Gao, Y., Xiang, X., Xiong, N., Huang, B., Lee, H.J., Alrifai, R., Jiang, X., Fang, Z.: Human action monitoring for healthcare based on deep learning. IEEE Access 6, 52277\u201352285 (2018)","journal-title":"IEEE Access"},{"issue":"7","key":"948_CR21","doi-asserted-by":"publisher","first-page":"6429","DOI":"10.1109\/JIOT.2020.2985082","volume":"7","author":"X Zhou","year":"2020","unstructured":"Zhou, X., Liang, W., Wang, K.I.-K., Wang, H., Yang, L.T., Jin, Q.: Deep-learning-enhanced human activity recognition for internet of healthcare things. IEEE Internet Things J. 7(7), 6429\u20136438 (2020)","journal-title":"IEEE Internet Things J."},{"issue":"1","key":"948_CR22","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1109\/JBHI.2019.2914970","volume":"24","author":"FJ Martinez-Murcia","year":"2020","unstructured":"Martinez-Murcia, F.J., Ortiz, A., Gorriz, J.-M., Ramirez, J., Castillo-Barnes, D.: Studying the manifold structure of Alzheimer\u2019s disease: a deep learning approach using convolutional autoencoders. IEEE J. Biomed. Health Inf. 24(1), 17\u201326 (2020)","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"948_CR23","doi-asserted-by":"publisher","first-page":"20021","DOI":"10.1109\/ACCESS.2018.2823979","volume":"6","author":"C Wu","year":"2018","unstructured":"Wu, C., Luo, C., Xiong, N., Zhang, W., Kim, T.-H.: A greedy deep learning method for medical disease analysis. IEEE Access 6, 20021\u201320030 (2018)","journal-title":"IEEE Access"},{"key":"948_CR24","unstructured":"Dijcks, JP.: \u201cOracle: Big data for the enterprise,\u201d 2012. [Online]. Available: http:\/\/www.oracle.com\/us\/products\/database\/big-data-for-enterprise-519135.pdf. [Accessed 1 December 2020]."},{"issue":"1","key":"948_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3150226","volume":"51","author":"S Pouyanfar","year":"2018","unstructured":"Pouyanfar, S., Yang, Y., Chen, S.-C., Shyu, M.-L., Iyengar, S.S.: Multimedia Big data analytics: a survey. ACM Comput. Surv. 51(1), 1\u201334 (2018)","journal-title":"ACM Comput. Surv."},{"key":"948_CR26","doi-asserted-by":"crossref","unstructured":"Halvorsen, P., Riegler, M.A., Schoeffmann, K.: \u201cMedical Multimedia Systems and Applications.\u201d 27th ACM International Conference on Multimedia. (2019)","DOI":"10.1145\/3343031.3351319"},{"key":"948_CR27","doi-asserted-by":"crossref","unstructured":"Hiriyannaiah, S., Akanksh, B.S., Koushik, A.S., Siddesh, G.M., Srinivasa, K.G.: \u201cDeep learning for multimedia data in IoT.\u201d Multimed. Big Data Comput. IoT Appl. pp. 101\u2013129. (2019)","DOI":"10.1007\/978-981-13-8759-3_4"},{"key":"948_CR28","doi-asserted-by":"publisher","first-page":"99152","DOI":"10.1109\/ACCESS.2019.2927134","volume":"7","author":"A Gumaei","year":"2019","unstructured":"Gumaei, A., Hassan, M.M., Alelaiwi, A., Alsalman, H.: A hybrid deep learning model for human activity recognition using multimodal body sensing data. IEEE Access 7, 99152\u201399160 (2019)","journal-title":"IEEE Access"},{"issue":"1","key":"948_CR29","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1109\/MMUL.2019.2897471","volume":"26","author":"S-C Chen","year":"2019","unstructured":"Chen, S.-C.: Multimedia deep learning. IEEE Multimed 26(1), 5\u20137 (2019)","journal-title":"IEEE Multimed"},{"issue":"1","key":"948_CR30","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1109\/TCBB.2017.2776910","volume":"16","author":"R Ju","year":"2017","unstructured":"Ju, R., Hu, C., Zhou, P., Li, Q.: Early diagnosis of Alzheimer\u2019s disease based on resting-state brain networks and deep learning. IEEE\/ACM Trans. Comput. Biol. Bioinf. 16(1), 244\u2013257 (2017)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"key":"948_CR31","doi-asserted-by":"publisher","first-page":"32258","DOI":"10.1109\/ACCESS.2018.2846609","volume":"6","author":"T Muhammed","year":"2018","unstructured":"Muhammed, T., Mehmood, R., Albeshri, A., Katib, I.: UbeHealth: a personalized ubiquitous cloud and edge-enabled networked healthcare system for smart cities. IEEE Access 6, 32258\u201332285 (2018)","journal-title":"IEEE Access"},{"issue":"10","key":"948_CR32","doi-asserted-by":"publisher","first-page":"5682","DOI":"10.1109\/TII.2019.2919168","volume":"15","author":"D Sierra-Sosa","year":"2019","unstructured":"Sierra-Sosa, D., Garcia-Zapirain, B., Castillo, C., Oleagordia, I., Nu\u00f1o-Solinis, R., Urtaran-Laresgoiti, M., Elmaghraby, A.: Scalable healthcare assessment for diabetic patients using deep learning on multiple GPUs. IEEE Trans. Ind. Inf. 15(10), 5682\u20135689 (2019)","journal-title":"IEEE Trans. Ind. Inf."},{"key":"948_CR33","doi-asserted-by":"crossref","unstructured":"Aderghal, K., Benois-Pineau, J., Afdel, K., Gwena\u00eblle, C.: \u201cFuseMe: classification of sMRI images by fusion of Deep CNNs in 2D+\u03b5 projections.\u201d 15th International Workshop on Content-Based Multimedia Indexing. (2017).","DOI":"10.1145\/3095713.3095749"},{"key":"948_CR34","unstructured":"Shan, F., Gao, Y., Wang, J., Shi, W., Shi N., Han, M., et. al., \u201cLung infection quantification of COVID-19 in CT images with deep learning.\u201d arXiv:2003.04655. (2020)."},{"issue":"2","key":"948_CR35","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1148\/radiol.2020200905","volume":"296","author":"L Li","year":"2020","unstructured":"Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., et al.: Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology 296(2), 65\u201367 (2020)","journal-title":"Radiology"},{"issue":"6","key":"948_CR36","doi-asserted-by":"publisher","first-page":"2775","DOI":"10.1109\/TCBB.2021.3065361","volume":"18","author":"Y Song","year":"2020","unstructured":"Song, Y., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z.: Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. IEEE\/ACM Trans. Comput. Biol. and Bioinf. 18(6), 2775\u20132780 (2020)","journal-title":"IEEE\/ACM Trans. Comput. Biol. and Bioinf."},{"key":"948_CR37","doi-asserted-by":"publisher","first-page":"118869","DOI":"10.1109\/ACCESS.2020.3005510","volume":"8","author":"S Hu","year":"2020","unstructured":"Hu, S., Gao, Y., Niu, Z., Jiang, Y., Li, L., Xiao, X., Wang, M., Fang, E.F., Ye, H.: Weakly supervised deep learning for COVID-19 infection detection and classification from CT images. IEEE Access 8, 118869\u2013118883 (2020)","journal-title":"IEEE Access"},{"key":"948_CR38","doi-asserted-by":"crossref","unstructured":"Shankar K, Eswaran P, Prayag T, et al.: \u201cDeep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images.\u201d Multimedia Systems. (2021)","DOI":"10.1007\/s00530-021-00800-x"},{"key":"948_CR39","doi-asserted-by":"crossref","unstructured":"Yazhini, K., Loganathan, D.: \u201cA state of art approaches on deep learning models in healthcare: an application perspective.\u201d 3rd International Conference on Trends in Electronics and Informatics (ICOEI), India. (2019)","DOI":"10.1109\/ICOEI.2019.8862730"},{"issue":"4","key":"948_CR40","doi-asserted-by":"publisher","first-page":"288","DOI":"10.26599\/BDMA.2019.9020007","volume":"2","author":"Y Yu","year":"2019","unstructured":"Yu, Y., Li, M., Liu, L., Li, Y., Wang, J.: Clinical big data and deep learning: applications, challenges, and future outlooks. Big Data Min. Anal. 2(4), 288\u2013305 (2019)","journal-title":"Big Data Min. Anal."},{"key":"948_CR41","doi-asserted-by":"crossref","unstructured":"Hung, C.Y., Lin, C.H., Chang, C.S., Li, J.L., Lee, C.C.: \u201cPredicting gastrointestinal bleeding events from multimodal in-hospital electronic health records using deep fusion networks.\u201d 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Germany. (2019)","DOI":"10.1109\/EMBC.2019.8857244"},{"issue":"1","key":"948_CR42","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/JBHI.2016.2636665","volume":"21","author":"D Ravi","year":"2017","unstructured":"Ravi, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.Z.: Deep learning for health informatics. IEEE Biomed. Health Inf. 21(1), 4\u201321 (2017)","journal-title":"IEEE Biomed. Health Inf."},{"issue":"6","key":"948_CR43","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1109\/MSP.2017.2738401","volume":"34","author":"D Ramachandram","year":"2017","unstructured":"Ramachandram, D., Taylor, G.W.: Deep multimodal learning: a survey on recent advances and trends. IEEE Signal Process. Mag. 34(6), 96\u2013108 (2017)","journal-title":"IEEE Signal Process. Mag."},{"key":"948_CR44","doi-asserted-by":"publisher","first-page":"10745","DOI":"10.1109\/ACCESS.2019.2891390","volume":"7","author":"SU Amin","year":"2019","unstructured":"Amin, S.U., Hossain, M.S., Muhammad, G., Alhussein, M., Rahman, M.A.: Cognitive smart healthcare for pathology detection and monitoring. IEEE Access 7, 10745\u201310753 (2019)","journal-title":"IEEE Access"},{"key":"948_CR45","unstructured":"LeCun Y., and Bengio, Y.: Convolutional networks for images, speech, and time series, in Handbook of Brain Theory and Neural Networks, USA: M. A. Arbib, ed. Cambridge, MA. (1995)"},{"issue":"4","key":"948_CR46","doi-asserted-by":"publisher","first-page":"1193","DOI":"10.1109\/TCBB.2018.2817488","volume":"16","author":"M Li","year":"2019","unstructured":"Li, M., Fei, Z., Zeng, M., Wu, F.-X., Li, Y., Pan, Y., Wang, J.: Automated ICD-9 coding via a deep learning approach. IEEE\/ACM Trans. Comput. Biol. Bioinf. 16(4), 1193\u20131202 (2019)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"key":"948_CR47","first-page":"6344","volume":"4","author":"W Yin","year":"2016","unstructured":"Yin, W., Yang, X., Zhang, L., Oki, E.: ECG monitoring system integrated with IR-UWB radar based on CNN. IEEE Access 4, 6344\u20136351 (2016)","journal-title":"IEEE Access"},{"issue":"3","key":"948_CR48","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1109\/MMUL.2018.2875861","volume":"25","author":"L Lu","year":"2018","unstructured":"Lu, L., Harrison, A.P.: Deep medical image computing in preventive and precision medicine. IEEE Multimedia 25(3), 109\u2013113 (2018)","journal-title":"IEEE Multimedia"},{"key":"948_CR49","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: \u201cDensely connected convolutional networks.\" 2017 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), USA, (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"948_CR50","doi-asserted-by":"publisher","first-page":"63373","DOI":"10.1109\/ACCESS.2019.2916887","volume":"7","author":"W Guo","year":"2019","unstructured":"Guo, W., Wang, J., Wang, S.: Deep multimodal representation learning: a survey. IEEE Access 7, 63373\u201363394 (2019)","journal-title":"IEEE Access"},{"key":"948_CR51","doi-asserted-by":"crossref","unstructured":"Zhang, S.F., Zhai, J.H., Xie, B.J., Zhan Y., Wang, X.: \u201cMultimodal representation learning: advances, trends and challenges.\u201d International Conference on Machine Learning and Cybernetics (ICMLC), Japan. (2019)","DOI":"10.1109\/ICMLC48188.2019.8949228"},{"key":"948_CR52","doi-asserted-by":"crossref","unstructured":"Eyben, F., W\u00f6llmer, M., Schuller, B.: \u201cOpensmile: the Munich versatile and fast open-source audio feature extractor.\u201d 18th ACM Int. Conf. Multimedia. (2010).","DOI":"10.1145\/1873951.1874246"},{"key":"948_CR53","doi-asserted-by":"crossref","unstructured":"El-Sawy, A., Bakry, H.E., Loey, M.: \u201cCNN for handwritten Arabic digits recognition based on LeNet-5.\u201d International Conference on Advanced Intelligent Systems and Informatics. (2016)","DOI":"10.1007\/978-3-319-48308-5_54"},{"issue":"3","key":"948_CR54","doi-asserted-by":"publisher","first-page":"483","DOI":"10.3390\/app9030483","volume":"9","author":"RA Minhas","year":"2019","unstructured":"Minhas, R.A., Javed, A., Irtaza, A., et al.: Shot classification of field sports videos using AlexNet convolutional neural network. Appl. Sci. 9(3), 483 (2019)","journal-title":"Appl. Sci."},{"issue":"6","key":"948_CR55","doi-asserted-by":"publisher","first-page":"1686","DOI":"10.1109\/JBHI.2019.2942774","volume":"24","author":"L Balagourouchetty","year":"2020","unstructured":"Balagourouchetty, L., Pragatheeswaran, J.K., Pottakkat, B., Ramkumar, G.: GoogLeNet-based ensemble FCNet classifier for focal liver lesion diagnosis. IEEE J. Biomed. Health Inf. 24(6), 1686\u20131694 (2020)","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"948_CR56","unstructured":"Simonyan K., Zisserman, A.: \u201cVery deep convolutional networks for large-scale image recognition.\u201d Computer Vision and Pattern Recognition. (2016)"},{"issue":"4","key":"948_CR57","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1109\/LSP.2018.2810121","volume":"25","author":"Z Lu","year":"2018","unstructured":"Lu, Z., Jiang, X., Kot, A.: Deep coupled resnet for low-resolution face recognition. IEEE Signal Process. Lett. 25(4), 526\u2013530 (2018)","journal-title":"IEEE Signal Process. Lett."},{"key":"948_CR58","doi-asserted-by":"crossref","unstructured":"Yang, M., Zhang, L., Feng, X., Zhang, D., \u201cFisher discrimination dictionary learning for sparse representation.\u201d International Conference on Computer Vision, Spain. (2011)","DOI":"10.1109\/ICCV.2011.6126286"},{"key":"948_CR59","doi-asserted-by":"crossref","unstructured":"Baltru\u0161aitis, T., Robinson, P., Morency, L.P., \u201cOpenFace: an open source facial behavior analysis toolkit.\u201d IEEE Winter Conference on Applications of Computer Vision (WACV). (2016)","DOI":"10.1109\/WACV.2016.7477553"},{"key":"948_CR60","doi-asserted-by":"crossref","unstructured":"Burlina, P., Freund, D.E., Joshi, N., Wolfson, Y., Bressler, N.M., \u201cDetection of age-related macular degeneration via deep learning.\u201d IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague. (2016)","DOI":"10.1109\/ISBI.2016.7493240"},{"issue":"1","key":"948_CR61","doi-asserted-by":"publisher","first-page":"1","DOI":"10.26599\/BDMA.2018.9020001","volume":"1","author":"J Liu","year":"2018","unstructured":"Liu, J., Pan, Y., Li, M., Chen, Z., Tang, L., Lu, C., Wang, J.: Applications of deep learning to MRI images: a survey. Big Data Min. Anal. 1(1), 1\u201318 (2018)","journal-title":"Big Data Min. Anal."},{"issue":"3","key":"948_CR62","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1007\/s11548-016-1501-5","volume":"12","author":"P Hu","year":"2017","unstructured":"Hu, P., Wu, F., Peng, J., Bao, Y., Chen, F., Kong, D.: Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets. Int. J. Comput. Assist Radiol. Surg. 12(3), 399\u2013411 (2017)","journal-title":"Int. J. Comput. Assist Radiol. Surg."},{"key":"948_CR63","doi-asserted-by":"crossref","unstructured":"Bar, Y., Diamant, I., Wolf L., Greenspan, H.:\u201cDeep learning with non-medical training used for chest pathology identification.\u201d Medical Imaging: Computer-Aided Diagnosis. (2015)","DOI":"10.1117\/12.2083124"},{"key":"948_CR64","doi-asserted-by":"crossref","unstructured":"Che, D., Safran, M., Peng, Z.: \u201cFrom Big data to big data mining: challenges, issues, and opportunities.\u201d International Conference on Database Systems for Advanced Applications. (2013)","DOI":"10.1007\/978-3-642-40270-8_1"},{"issue":"2","key":"948_CR65","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.ijinfomgt.2014.10.007","volume":"35","author":"A Gandomi","year":"2015","unstructured":"Gandomi, A., Haider, M.: Beyond the hype: Big data concepts, methods, and analytics. Int. J. Inf. Manag. 35(2), 133\u2013144 (2015)","journal-title":"Int. J. Inf. Manag."},{"issue":"9","key":"948_CR66","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0162721","volume":"11","author":"Z Ye","year":"2016","unstructured":"Ye, Z., Tafti, A.P., He, K.Y., Wang, K., He, M.M.: Sparktext: biomedical text mining on big data framework. PLoS ONE 11(9), e0162721 (2016)","journal-title":"PLoS ONE"},{"key":"948_CR67","doi-asserted-by":"crossref","unstructured":"Leibetseder, A., Petscharnig, S., Primus M.J., et. Al.: \u201cLapgyn4: a dataset for 4 automatic content analysis problems in the domain of laparoscopic gynecology.\u201d 9th ACM Multimedia Systems Conference. (2018)","DOI":"10.1145\/3204949.3208127"},{"key":"948_CR68","doi-asserted-by":"crossref","unstructured":"Pogorelov, K., Randel, K.R., de Lange, T., et. al, \u201cNerthus: a bowel preparation quality video dataset.\u201d 8th ACM on Multimedia Systems Conference. (2017)","DOI":"10.1145\/3193165"},{"key":"948_CR69","doi-asserted-by":"crossref","unstructured":"Pogorelov, K., Randel, K.R., Griwodz C., et Al.: \u201cKvasir: a multi-class image data set for computer aided gastrointestinal disease detection.\u201d ACM Multimedia Systems(MMSYS). (2017)","DOI":"10.1145\/3193289"},{"key":"948_CR70","doi-asserted-by":"crossref","unstructured":"Schoeffmann, K., Taschwer, M., Sarny, S., et al., \u201cCataract-101--video dataset of101 cataract surgeries.\u201d ACM International Conference on Multimedia Retrieval (ICMR). (2018)","DOI":"10.1145\/3204949.3208137"},{"issue":"1","key":"948_CR71","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1109\/JBHI.2016.2633963","volume":"21","author":"P Nguyen","year":"2017","unstructured":"Nguyen, P., Tran, T., Wickramasinghe, N., Venkatesh, S.: Deepr: a convolutional net for medical records. IEEE J. Biomed. Health Inf. 21(1), 22\u201330 (2017)","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"948_CR72","unstructured":"Choi, E., Bahadori, M.T., Schuetz, A., Stewart, W.F., Sun, J., \u201cDoctor ai: Predicting clinical events via recurrent neural networks.\u201d 1st Mach. Learn. Healthcare Conf. (2016)"},{"key":"948_CR73","doi-asserted-by":"publisher","first-page":"115383","DOI":"10.1109\/ACCESS.2020.3003424","volume":"8","author":"H Guo","year":"2020","unstructured":"Guo, H., Zhang, Y.: Resting state fMRI and improved deep learning algorithm for earlier detection of Alzheimer\u2019s disease. IEEE Access 8, 115383\u2013115392 (2020)","journal-title":"IEEE Access"},{"issue":"10223","key":"948_CR74","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., et al.: Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet 395(10223), 497\u2013506 (2020)","journal-title":"The Lancet"},{"issue":"11","key":"948_CR75","doi-asserted-by":"publisher","first-page":"1061","DOI":"10.1001\/jama.2020.1585","volume":"323","author":"D Wang","year":"2020","unstructured":"Wang, D., Hu, B., Hu, C., et al.: Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA 323(11), 1061 (2020)","journal-title":"JAMA"},{"key":"948_CR76","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1016\/j.ins.2020.09.041","volume":"545","author":"S Varela-Santos","year":"2020","unstructured":"Varela-Santos, S., Melin, P.: A new approach for classifying coronavirus COVID-19 based on its manifestation on chest X-rays using texture features and neural networks. Inf. Sci. 545, 403\u2013414 (2020)","journal-title":"Inf. Sci."},{"key":"948_CR77","unstructured":"Bankman, I.: Handbook of medical image processing and analysis, San Diego, CA, USA: second ed., Academic Press. (2008)"},{"key":"948_CR78","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114054","volume":"164","author":"AM Ismael","year":"2020","unstructured":"Ismael, A.M., Seng\u00fcr, A.: Deep learning approaches for COVID-19 detection based on chest X-ray images. Exp Syst. Appl. 164, 114054 (2020)","journal-title":"Exp Syst. Appl."},{"key":"948_CR79","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.inffus.2020.11.005","volume":"68","author":"S-H Wang","year":"2020","unstructured":"Wang, S.-H., Nayak, D.R., Guttery, D.S., et al.: COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis. Inf. Fusio 68, 131\u2013148 (2020)","journal-title":"Inf. Fusio"},{"key":"948_CR80","doi-asserted-by":"publisher","first-page":"107700","DOI":"10.1016\/j.patcog.2020.107700","volume":"113","author":"M Shorfuzzaman","year":"2020","unstructured":"Shorfuzzaman, M., and Hossain, M.S.: MetaCOVID: a siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients. Pattern Recognit. 113, 107700 (2020)","journal-title":"Pattern Recognit."},{"issue":"4","key":"948_CR81","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1109\/MNET.011.2000458","volume":"34","author":"MS Hossain","year":"2020","unstructured":"Hossain, M.S., Muhammad, G., Guizani, N.: Explainable AI and mass surveillance system-based healthcare framework to combat COVID-i9 like pandemics. IEEE Netw. 34(4), 126\u2013132 (2020)","journal-title":"IEEE Netw."},{"key":"948_CR82","doi-asserted-by":"publisher","first-page":"2643","DOI":"10.1109\/ACCESS.2018.2879117","volume":"7","author":"R Yunus","year":"2018","unstructured":"Yunus, R., Arif, O., Afzal, H., Amjad, M.F., Abbas, H., Bokhari, H.N., et al.: A framework to estimate the nutritional value of food in real time using deep learning techniques. IEEE Access 7, 2643\u20132652 (2018)","journal-title":"IEEE Access"},{"key":"948_CR83","unstructured":"Mikolov, T., Chen, K., Corrado G., Dean, J., \u201cEfficient estimation of word representations in vector space.\u201d Computation and Language. (2013)"},{"key":"948_CR84","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z., \u201cRethinking the inception architecture for computer vision.\u201d IEEE Conference on Computer Vision and Pattern Recognition (CVPR). (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"948_CR85","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: \u201cInception-v4, Inception-ResNet and the impact of residual connections on learning.\u201d Computer Vision and Pattern Recognition. (2016)","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"948_CR86","unstructured":"Cheng, G., Wan, Y., Saudagar, A.N., Namuduri, K., Buckles, B.P.: \u201cAdvances in human action recognition: a survey.\u201d Computer Vision and Pattern Recognition. (2015)"},{"issue":"1","key":"948_CR87","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1109\/TMM.2017.2726187","volume":"20","author":"EA Bernal","year":"2018","unstructured":"Bernal, E.A., Yang, X., Li, Q., Kumar, J., Madhvanath, S., Ramesh, P., Bala, R.: Deep temporal multimodal fusion for medical procedure monitoring using wearable sensors. IEEE Trans. Multimed. 20(1), 107\u2013118 (2018)","journal-title":"IEEE Trans. Multimed."},{"key":"948_CR88","doi-asserted-by":"crossref","unstructured":"Kumar, J., Li, Q., Kyal, S., Bernal, E.A., Bala, R.: \u201cOn-the-Fly Hand detection training with application in egocentric action recognition.\u201d IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. (2015)","DOI":"10.1109\/CVPRW.2015.7301344"},{"key":"948_CR89","doi-asserted-by":"crossref","unstructured":"Shojaei-Hashemi, A., Nasiopoulos, P., Little, J.J., Pourazad, M.T., \u201cVideo-based human fall detection in smart homes using deep learning.\u201d IEEE International Symposium on Circuits and Systems (ISCAS), Italy. (2018)","DOI":"10.1109\/ISCAS.2018.8351648"},{"key":"948_CR90","doi-asserted-by":"crossref","unstructured":"Shahroudy, A., Liu, J., Ng, T.T., Wang, G.:\u201cNTU RGB+D: a large scale dataset for 3d human activity analysis.\u201d IEEE Conference on Computer Vision and Pattern Recognition (CVPR). (2016)","DOI":"10.1109\/CVPR.2016.115"},{"key":"948_CR91","unstructured":"Muhammad, K., Khan, S., Ser, J.D., de Albuquerque, VHC.: \u201cDeep learning for multigrade brain tumor classification in smart healthcare systems: a prospective survey.\u201d IEEE Transactions on Neural Networks and Learning Systems. Early Access. pp. 1\u201316 (2020)."},{"key":"948_CR92","doi-asserted-by":"crossref","unstructured":"Abadi, M.: \u201cTensorFlow: learning functions at scale.\u201d 21st ACM SIGPLAN International Conference on Functional. (2016)","DOI":"10.1145\/2951913.2976746"},{"key":"948_CR93","doi-asserted-by":"crossref","unstructured":"Rasiwasia, N., Pereira, J.C., Coviello E., et. al: \u201cA new approach to cross-modal multimedia retrieval.\u201d 18th ACM international conference on Multimedia. (2010)","DOI":"10.1145\/1873951.1873987"},{"issue":"4","key":"948_CR94","doi-asserted-by":"publisher","first-page":"960","DOI":"10.1109\/TCYB.2016.2535122","volume":"47","author":"J Zhang","year":"2017","unstructured":"Zhang, J., Han, Y., Tang, J., Hu, Q., Jiang, J.: Semi-supervised image-to-video adaptation for video action recognition. IEEE Trans. Cybern. 47(4), 960\u2013973 (2017)","journal-title":"IEEE Trans. Cybern."},{"issue":"10","key":"948_CR95","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S.J., Yang, Q.: A Survey on Transfer Learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2010)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"948_CR96","doi-asserted-by":"crossref","unstructured":"He K., Zhang, X., Ren, S., Sun, J.: \u201cDeep residual learning for image recognition.\u201d IEEE Conference on Computer Vision and Pattern Recognition (CVPR). (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"948_CR97","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C. D.: \u201cGloVe: Global vectors for word representation.\u201d Conf. Empirical Methods Natural Lang. Process. (2014).","DOI":"10.3115\/v1\/D14-1162"},{"key":"948_CR98","doi-asserted-by":"crossref","unstructured":"Riegler, M., Lux, M., Griwodz C., et. Al: \u201cMultimedia and medicine: teammates for better disease detection and survival.\u201d 24th ACM international conference on Multimedia. (2016)","DOI":"10.1145\/2964284.2976760"},{"issue":"2","key":"948_CR99","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1109\/MMUL.2018.023121167","volume":"25","author":"AE Saddik","year":"2018","unstructured":"Saddik, A.E.: Digital twins: the convergence of multimedia technologies. IEEE Multimedia 25(2), 87\u201392 (2018)","journal-title":"IEEE Multimedia"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-022-00948-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-022-00948-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-022-00948-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T12:02:36Z","timestamp":1744200156000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-022-00948-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,24]]},"references-count":99,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["948"],"URL":"https:\/\/doi.org\/10.1007\/s00530-022-00948-0","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,24]]},"assertion":[{"value":"10 February 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 May 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}