{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T13:42:32Z","timestamp":1780494152723,"version":"3.54.1"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,7,3]],"date-time":"2021-07-03T00:00:00Z","timestamp":1625270400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,7,3]],"date-time":"2021-07-03T00:00:00Z","timestamp":1625270400000},"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":["Ann Oper Res"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1007\/s10479-021-04154-5","type":"journal-article","created":{"date-parts":[[2021,7,3]],"date-time":"2021-07-03T09:02:21Z","timestamp":1625302941000},"page":"289-309","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Depth-wise dense neural network for automatic COVID19 infection detection and diagnosis"],"prefix":"10.1007","volume":"360","author":[{"given":"Abdul","family":"Qayyum","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3930-6600","authenticated-orcid":false,"given":"Imran","family":"Razzak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M.","family":"Tanveer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ajay","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,7,3]]},"reference":[{"key":"4154_CR1","doi-asserted-by":"crossref","unstructured":"Afshar, P., Heidarian, S., Naderkhani, F., Oikonomou, A., Plataniotis, K. N., & Mohammadi, A. (2020). Covid-caps: A capsule network-based framework for identification of covid-19 cases from X-ray images. arXiv preprint arXiv:2004.02696","DOI":"10.3389\/frai.2021.598932"},{"key":"4154_CR2","doi-asserted-by":"publisher","first-page":"110071","DOI":"10.1016\/j.chaos.2020.110071","volume":"140","author":"A Altan","year":"2020","unstructured":"Altan, A., & Karasu, S. (2020). Recognition of covid-19 disease from X-ray images by hybrid model consisting of 2d curvelet transform, chaotic salp swarm algorithm and deep learning technique. Chaos, Solitons & Fractals, 140, 110071.","journal-title":"Chaos, Solitons & Fractals"},{"key":"4154_CR3","doi-asserted-by":"crossref","unstructured":"Al-Timemy, A. H., Khushaba, R. N., Mosa, Z. M., & Escudero, J. (2020). An efficient mixture of deep and machine learning models for covid-19 and tuberculosis detection using X-ray images in resource limited settings. arXiv preprint arXiv:2007.08223","DOI":"10.1007\/978-3-030-69744-0_6"},{"key":"4154_CR4","doi-asserted-by":"publisher","first-page":"104037","DOI":"10.1016\/j.compbiomed.2020.104037","volume":"126","author":"A Amyar","year":"2020","unstructured":"Amyar, A., Modzelewski, R., Li, H., & Ruan, S. (2020). Multi-task deep learning based CT imaging analysis for covid-19 pneumonia: Classification and segmentation. Computers in Biology and Medicine, 126, 104037.","journal-title":"Computers in Biology and Medicine"},{"key":"4154_CR5","unstructured":"Bizopoulos, P., Vretos, N., & Daras, P. (2020). Comprehensive comparison of deep learning models for lung and covid-19 lesion segmentation in CT scans. arXiv preprint arXiv:2009.06412"},{"key":"4154_CR6","doi-asserted-by":"crossref","unstructured":"Born, J., Wiedemann, N., Brandle, G., Buhre, C., Rieck, B., & Borgwardt, K. (2020). Accelerating covid-19 differential diagnosis with explainable ultrasound image analysis. arXiv preprint arXiv:2009.06116","DOI":"10.1136\/thorax-2020-BTSabstracts.404"},{"issue":"2","key":"4154_CR7","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1109\/TMI.2013.2290491","volume":"33","author":"S Candemir","year":"2013","unstructured":"Candemir, S., Jaeger, S., Palaniappan, K., Musco, J. P., Singh, R. K., Xue, Z., Karargyris, A., Antani, S., Thoma, G., & McDonald, C. J. (2013). Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Transactions on Medical Imaging, 33(2), 577\u2013590.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"4154_CR8","doi-asserted-by":"crossref","unstructured":"Chen, J., Wu, L., Zhang, J., Zhang, L., Gong, D., Zhao, Y., Hu, S., Wang, Y., Hu, X., Zheng, B., Wu, H., Dong, Z., Xu, Y., Zhu, Y., Chen, X., Zhang, M., Yu, L., Cheng, F., & Yu, H. (2020). Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: A prospective study. MedRxiv.","DOI":"10.1101\/2020.02.25.20021568"},{"key":"4154_CR9","doi-asserted-by":"crossref","unstructured":"Chowdhury, M. E. H., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M. A., Mahbub, Z. B., Islam, K. R., Khan, M. S., Iqbal, A., Al- Emadi, N., Reaz, M. B. I., & Islam, M. T. (2020). Can AI help in screening viral and covid-19 pneumonia? arXiv preprint arXiv:2003.13145","DOI":"10.1109\/ACCESS.2020.3010287"},{"key":"4154_CR10","doi-asserted-by":"crossref","unstructured":"Cohen, J. P., Morrison, P., Dao, L., Roth, K., Duong, T. Q., & Ghassemi, M. (2020). Covid-19 image data collection: Prospective predictions are the future. arXiv preprint arXiv:2006.11988","DOI":"10.59275\/j.melba.2020-48g7"},{"key":"4154_CR11","unstructured":"Das, N. N., Kumar, N., Kaur, M., Kumar, V., & Singh, D. (2020). Automated deep transfer learning-based approach for detection of covid-19 infection in chest X-rays. IRBM."},{"key":"4154_CR12","doi-asserted-by":"crossref","unstructured":"Elharrouss, O., Subramanian, N., & Al-Maadeed, S. (2020). An encoder\u2013decoder-based method for covid-19 lung infection segmentation. arXiv preprint arXiv:2007.00861","DOI":"10.29117\/quarfe.2020.0294"},{"key":"4154_CR13","first-page":"497","volume":"395","author":"D-P Fan","year":"2020","unstructured":"Fan, D.-P., Zhou, T., Ji, G.-P., Zhou, Y., Chen, G., Fu, H., Shen, J., & Shao, L. (2020). Inf-Net: Automatic covid-19 lung infection segmentation from CT images. IEEE Transactions on Medical Imaging, 395, 497.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"4154_CR14","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1007\/978-3-030-62469-9_8","volume-title":"Thoracic image analysis","author":"O Gozes","year":"2020","unstructured":"Gozes, O., Frid-Adar, M., Sagie, N., Kabakovitch, A., Amran, D., Amer, R., & Greenspan, H. (2020). A weakly supervised deep learning framework for covid-19 CT detection and analysis. In J. Petersen, R. S. J. Est\u00e9par, A. Schmidt-Richberg, S. Gerard, B. Lassen-Schmidt, C. Jacobs, R. Beichel, & K. Mori (Eds.), Thoracic image analysis (pp. 84\u201393). Springer International Publishing."},{"key":"4154_CR15","unstructured":"Hinton, G. E., Sabour, S., & Frosst, N. (2018). Matrix capsules with em routing. In International conference on learning representations."},{"key":"4154_CR16","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7132\u20137141).","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"2","key":"4154_CR17","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1109\/TMI.2013.2284099","volume":"33","author":"S Jaeger","year":"2013","unstructured":"Jaeger, S., Karargyris, A., Candemir, S., Folio, L., Siegelman, J., Callaghan, F., Xue, Z., Palaniappan, K., Singh, R. K., Antani, S., & Thoma, G. (2013). Automatic tuberculosis screening using chest radiographs. IEEE Transactions on Medical Imaging, 33(2), 233\u2013245.","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"1","key":"4154_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0192-5","volume":"6","author":"JM Johnson","year":"2019","unstructured":"Johnson, J. M., & Khoshgoftaar, T. M. (2019). Survey on deep learning with class imbalance. J Big Data, 6(1), 1\u201354.","journal-title":"J Big Data"},{"issue":"5","key":"4154_CR19","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","volume":"172","author":"DS Kermany","year":"2018","unstructured":"Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., Dong, J., Prasadha, M. K., Pei, J., Ting, M. Y. L., Zhu, J., Li, C., Hewett, S., Dong, J., Ziyar, I., \u2026 Zhang, K. (2018). Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell, 172(5), 1122\u20131131.","journal-title":"Cell"},{"key":"4154_CR20","doi-asserted-by":"publisher","first-page":"105581","DOI":"10.1016\/j.cmpb.2020.105581","volume":"196","author":"AI Khan","year":"2020","unstructured":"Khan, A. I., Shah, J. L., & Bhat, M. M. (2020). Coronet: A deep neural network for detection and diagnosis of covid-19 from chest X-ray images. Computer Methods and Programs in Biomedicine, 196, 105581.","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"4154_CR21","doi-asserted-by":"crossref","unstructured":"Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., Bai, J., Lu, Y., Fang, Z., Song, Q., & Cao, K. (2020). Artificial intelligence distinguishes covid-19 from community acquired pneumonia on chest CT. Radiology.","DOI":"10.1148\/radiol.2020200905"},{"key":"4154_CR22","doi-asserted-by":"crossref","unstructured":"Li, T., Wang, Z., Chen, Y., Zhang, L., Gao, Y., Shi, F., Qian, D., Wang, Q., & Shen, D. (2020). Two-stage mapping-segmentation framework for delineating covid-19 infections from heterogeneous CT images. In International workshop on thoracic image analysis (Vol. 65, pp. 3\u201313). Springer.","DOI":"10.1007\/978-3-030-62469-9_1"},{"key":"4154_CR23","doi-asserted-by":"publisher","first-page":"225034","DOI":"10.1088\/1361-6560\/abc04e","volume":"65","author":"J Ma","year":"2020","unstructured":"Ma, J., Nie, Z., Wang, C., Dong, G., Zhu, Q., He, J., Gui, L., & Yang, X. (2020). Active contour regularized semi-supervised learning for covid-19 CT infection segmentation with limited annotations. Physics in Medicine Biology, 65, 225034.","journal-title":"Physics in Medicine Biology"},{"key":"4154_CR24","doi-asserted-by":"publisher","first-page":"103869","DOI":"10.1016\/j.compbiomed.2020.103869","volume":"122","author":"T Mahmud","year":"2020","unstructured":"Mahmud, T., Rahman, M. A., & Fattah, S. A. (2020). CovXNet: A multi-dilation convolutional neural network for automatic covid-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization. Computers in Biology and Medicine, 122, 103869.","journal-title":"Computers in Biology and Medicine"},{"key":"4154_CR25","doi-asserted-by":"crossref","unstructured":"Narin, A., Kaya, C., & Pamuk, Z. (2020). Automatic detection of coro-navirus disease (covid-19) using X-ray images and deep convolutional neural networks. arXiv preprint arXiv:2003.10849","DOI":"10.1007\/s10044-021-00984-y"},{"key":"4154_CR26","doi-asserted-by":"publisher","first-page":"107747","DOI":"10.1016\/j.patcog.2020.107747","volume":"114","author":"A Oulefki","year":"2020","unstructured":"Oulefki, A., Agaian, S., Trongtirakul, T., & Laouar, A. K. (2020). Automatic covid-19 lung infected region segmentation and measurement using CT-scans images. Pattern Recognition, 114, 107747.","journal-title":"Pattern Recognition"},{"key":"4154_CR27","doi-asserted-by":"publisher","first-page":"103792","DOI":"10.1016\/j.compbiomed.2020.103792","volume":"121","author":"T Ozturk","year":"2020","unstructured":"Ozturk, T., Talo, M., Yildirim, E. A., Baloglu, U. B., Yildirim, O., & Acharya, U. R. (2020). Automated detection of covid-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine, 121, 103792.","journal-title":"Computers in Biology and Medicine"},{"key":"4154_CR28","doi-asserted-by":"publisher","first-page":"109944","DOI":"10.1016\/j.chaos.2020.109944","volume":"138","author":"H Panwar","year":"2020","unstructured":"Panwar, H., Gupta, P. K., Siddiqui, M. K., Morales-Menendez, R., & Singh, V. (2020). Application of deep learning for fast detection of covid-19 in X-rays using ncovnet. Chaos, Solitons & Fractals, 138, 109944.","journal-title":"Chaos, Solitons & Fractals"},{"key":"4154_CR29","unstructured":"Razzak, I., Naz, S., Rehman, A., Khan, A., & Zaib, A. (2020a). Improving coronavirus (covid-19) diagnosis using deep transfer learning. medRxiv."},{"issue":"9","key":"4154_CR30","doi-asserted-by":"publisher","first-page":"4417","DOI":"10.1007\/s00521-019-04095-y","volume":"32","author":"MI Razzak","year":"2020","unstructured":"Razzak, M. I., Imran, M., & Xu, G. (2020b). Big data analytics for preventive medicine. Neural Computing and Applications, 32(9), 4417\u20134451.","journal-title":"Neural Computing and Applications"},{"key":"4154_CR31","doi-asserted-by":"crossref","unstructured":"Roy, A. G., Navab, N., & Wachinger, C. (2018). Concurrent spatial and channel \u2018squeeze and excitation\u2019 in fully convolutional net-works. In International conference on medical image computing and computer-assisted intervention (pp. 421\u2013429). Springer.","DOI":"10.1007\/978-3-030-00928-1_48"},{"key":"4154_CR32","doi-asserted-by":"crossref","unstructured":"Saeedizadeh, N., Minaee, S., Kafieh, R., Yazdani, S., & Sonka, M. (2020). Covid TV-Unet: Segmenting covid-19 chest CT images using connectivity imposed U-net. arXiv preprint arXiv:2007.12303","DOI":"10.1016\/j.cmpbup.2021.100007"},{"key":"4154_CR33","first-page":"2020","volume":"2020030300","author":"PK Sethy","year":"2020","unstructured":"Sethy, P. K., & Behera, S. K. (2020). Detection of coronavirus disease (covid-19) based on deep features. Preprints, 2020030300, 2020.","journal-title":"Preprints"},{"issue":"8","key":"4154_CR34","doi-asserted-by":"publisher","first-page":"2653","DOI":"10.1109\/TMI.2020.3000314","volume":"39","author":"G Wang","year":"2020","unstructured":"Wang, G., Liu, X., Li, C., Xu, Z., Ruan, J., Zhu, H., Meng, T., Li, K., Huang, N., & Zhang, S. (2020a). A noise-robust framework for automatic segmentation of covid-19 pneumonia lesions from CT images. IEEE Transactions on Medical Imaging, 39(8), 2653\u20132663.","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"1","key":"4154_CR35","first-page":"1","volume":"10","author":"L Wang","year":"2020","unstructured":"Wang, L., Lin, Z. Q., & Wong, A. (2020b). Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest X-ray images. Scientific Reports, 10(1), 1\u201312.","journal-title":"Scientific Reports"},{"issue":"8","key":"4154_CR36","doi-asserted-by":"publisher","first-page":"2664","DOI":"10.1109\/TMI.2020.2995108","volume":"39","author":"W Xie","year":"2020","unstructured":"Xie, W., Jacobs, C., Charbonnier, J. P., & van Ginneken, B. (2020). Relational modeling for robust and efficient pulmonary lobe segmentation in CT scans. IEEE Transactions on Medical Imaging, 39(8), 2664\u20132675.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"4154_CR37","unstructured":"Xu, Z., Cao, Y., Jin, C., Shao, G., Liu, X., Zhou, J., Shi, H., & Feng, J. (2020). GASNet: Weakly-supervised framework for covid-19 lesion segmentation. arXiv preprint arXiv:2010.09456"},{"key":"4154_CR38","doi-asserted-by":"crossref","unstructured":"Yao, Q., Xiao, L., Liu, P., & Zhou, S. K. (2020). Label-free segmentation of covid-19 lesions in lung CT. arXiv preprint arXiv:2009.06456","DOI":"10.1109\/TMI.2021.3066161"},{"key":"4154_CR39","doi-asserted-by":"publisher","first-page":"185786","DOI":"10.1109\/ACCESS.2020.3027738","volume":"8","author":"B Zheng","year":"2020","unstructured":"Zheng, B., Liu, Y., Zhu, Y., Yu, F., Jiang, T., Yang, D., & Xu, T. (2020). MSD-Net: Multi-scale discriminative network for covid-19 lung infection segmentation on CT. IEEE Access, 8, 185786\u2013185795.","journal-title":"IEEE Access"}],"container-title":["Annals of Operations Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-021-04154-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10479-021-04154-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-021-04154-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T15:21:19Z","timestamp":1777476079000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10479-021-04154-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,3]]},"references-count":39,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["4154"],"URL":"https:\/\/doi.org\/10.1007\/s10479-021-04154-5","relation":{},"ISSN":["0254-5330","1572-9338"],"issn-type":[{"value":"0254-5330","type":"print"},{"value":"1572-9338","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,3]]},"assertion":[{"value":"8 June 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 July 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}