{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T04:06:19Z","timestamp":1774497979258,"version":"3.50.1"},"reference-count":33,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T00:00:00Z","timestamp":1673827200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Science and Engineering Research Board (SERB), Department of Science and Technology, Govt. of India","award":["SRG\/2020\/001460"],"award-info":[{"award-number":["SRG\/2020\/001460"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Manage. Inf. Syst."],"published-print":{"date-parts":[[2023,3,31]]},"abstract":"<jats:p>\n            Recent years have witnessed a rise in employing deep learning methods, especially\n            <jats:bold>convolutional neural networks (CNNs)<\/jats:bold>\n            for detection of COVID-19 cases using chest CT scans. Most of the state-of-the-art models demand a huge amount of parameters which often suffer from overfitting in the presence of limited training samples such as chest CT data and thereby, reducing the detection performance. To handle these issues, in this paper, a\n            <jats:bold>lightweight multi-scale CNN<\/jats:bold>\n            called\n            <jats:bold>LiMS-Net<\/jats:bold>\n            is proposed. The LiMS-Net contains two feature learning blocks where, in each block, filters of different sizes are applied in parallel to derive multi-scale features from the suspicious regions and an additional filter is subsequently employed to capture discriminant features. The model has only 2.53M parameters and therefore, requires low computational cost and memory space when compared to pretrained CNN architectures. Comprehensive experiments are carried out using a publicly available COVID-19 CT dataset and the results demonstrate that the proposed model achieves higher performance than many pretrained CNN models and state-of-the-art methods even in the presence of limited CT data. Our model achieves an accuracy of 92.11% and an F1-score of 92.59% for detection of COVID-19 from CT scans. Further, the results on a relatively larger CT dataset indicate the effectiveness of the proposed model.\n          <\/jats:p>","DOI":"10.1145\/3551647","type":"journal-article","created":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T11:20:51Z","timestamp":1658920851000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["LiMS-Net: A Lightweight Multi-Scale CNN for COVID-19 Detection from Chest CT Scans"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6747-3780","authenticated-orcid":false,"given":"Amogh Manoj","family":"Joshi","sequence":"first","affiliation":[{"name":"Vivekanand Education Society\u2019s Institute of Technology, Mumbai, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8929-5778","authenticated-orcid":false,"given":"Deepak Ranjan","family":"Nayak","sequence":"additional","affiliation":[{"name":"Malaviya National Institute of Technology, Jaipur, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0285-2560","authenticated-orcid":false,"given":"Dibyasundar","family":"Das","sequence":"additional","affiliation":[{"name":"National Institute of Technology, Rourkela, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4870-1493","authenticated-orcid":false,"given":"Yudong","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Leicester, Leicester, UK"}]}],"member":"320","published-online":{"date-parts":[[2023,1,16]]},"reference":[{"key":"e_1_3_2_2_2","article-title":"SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification","author":"Angelov Plamen","year":"2020","unstructured":"Plamen Angelov and Eduardo Almeida Soares. 2020. SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification. MedRxiv (2020).","journal-title":"MedRxiv"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200230"},{"key":"e_1_3_2_5_2","article-title":"Show your work: Improved reporting of experimental results","author":"Dodge Jesse","year":"2019","unstructured":"Jesse Dodge, Suchin Gururangan, Dallas Card, Roy Schwartz, and Noah A. Smith. 2019. Show your work: Improved reporting of experimental results. arXiv preprint arXiv:1909.03004 (2019).","journal-title":"arXiv preprint arXiv:1909.03004"},{"key":"e_1_3_2_6_2","article-title":"The role of imaging in the detection and management of COVID-19: A review","author":"Dong Di","year":"2020","unstructured":"Di Dong, Zhenchao Tang, Shuo Wang, Hui Hui, Lixin Gong, Yao Lu, Zhong Xue, Hongen Liao, Fang Chen, Fan Yang, et\u00a0al. 2020. The role of imaging in the detection and management of COVID-19: A review. IEEE Reviews in Biomedical Engineering (2020).","journal-title":"IEEE Reviews in Biomedical Engineering"},{"key":"e_1_3_2_7_2","first-page":"200432","article-title":"Sensitivity of chest CT for COVID-19: Comparison to RT-PCR","author":"Fang Yicheng","year":"2020","unstructured":"Yicheng Fang, Huangqi Zhang, Jicheng Xie, Minjie Lin, Lingjun Ying, Peipei Pang, and Wenbin Ji. 2020. Sensitivity of chest CT for COVID-19: Comparison to RT-PCR. Radiology (2020), 200432.","journal-title":"Radiology"},{"key":"e_1_3_2_8_2","article-title":"CVR-Net: A deep convolutional neural network for Coronavirus recognition from chest radiography images","author":"Hasan Md.","year":"2020","unstructured":"Md. Hasan, Md. Alam, Md. Elahi, E. Toufick, Shidhartho Roy, Sifat Redwan Wahid, et\u00a0al. 2020. CVR-Net: A deep convolutional neural network for Coronavirus recognition from chest radiography images. arXiv preprint arXiv:2007.11993 (2020).","journal-title":"arXiv preprint arXiv:2007.11993"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_10_2","article-title":"Sample-efficient deep learning for COVID-19 diagnosis based on CT scans","author":"He Xuehai","year":"2020","unstructured":"Xuehai He, Xingyi Yang, Shanghang Zhang, Jinyu Zhao, Yichen Zhang, Eric Xing, and Pengtao Xie. 2020. Sample-efficient deep learning for COVID-19 diagnosis based on CT scans. MedRxiv (2020).","journal-title":"MedRxiv"},{"key":"e_1_3_2_11_2","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"Howard Andrew G.","year":"2017","unstructured":"Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017).","journal-title":"arXiv preprint arXiv:1704.04861"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"e_1_3_2_13_2","article-title":"COVID-19 CT image synthesis with a conditional generative adversarial network","author":"Jiang Yifan","year":"2020","unstructured":"Yifan Jiang, Han Chen, M. H. Loew, and Hanseok Ko. 2020. COVID-19 CT image synthesis with a conditional generative adversarial network. IEEE Journal of Biomedical and Health Informatics (2020).","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"e_1_3_2_14_2","article-title":"Automated diagnosis of COVID-19 using deep features and parameter free BAT optimization","author":"Kaur Taranjit","year":"2021","unstructured":"Taranjit Kaur, Tapan K. Gandhi, and Bijaya K. Panigrahi. 2021. Automated diagnosis of COVID-19 using deep features and parameter free BAT optimization. IEEE Journal of Translational Engineering in Health and Medicine (2021).","journal-title":"IEEE Journal of Translational Engineering in Health and Medicine"},{"key":"e_1_3_2_15_2","first-page":"531","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"Li Shaohua","year":"2019","unstructured":"Shaohua Li, Yong Liu, Xiuchao Sui, Cheng Chen, Gabriel Tjio, Daniel Shu Wei Ting, and Rick Siow Mong Goh. 2019. Multi-instance multi-scale CNN for medical image classification. In International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 531\u2013539."},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12967-020-02324-w"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-020-0931-3"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102365"},{"key":"e_1_3_2_19_2","article-title":"Artificial intelligence in the battle against Coronavirus (COVID-19): A survey and future research directions","author":"Nguyen Thanh Thi","year":"2020","unstructured":"Thanh Thi Nguyen, Quoc Viet Hung Nguyen, Dung Tien Nguyen, Edbert B. Hsu, Samuel Yang, and Peter Eklund. 2020. Artificial intelligence in the battle against Coronavirus (COVID-19): A survey and future research directions. arXiv preprint arXiv:2008.07343 (2020).","journal-title":"arXiv preprint arXiv:2008.07343"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2993291"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2020.10.001"},{"key":"e_1_3_2_22_2","article-title":"Swish: A self-gated activation function","author":"Ramachandran Prajit","year":"2017","unstructured":"Prajit Ramachandran, Barret Zoph, and Quoc V. Le. 2017. Swish: A self-gated activation function. arXiv preprint arXiv:1710.05941 (2017).","journal-title":"arXiv preprint arXiv:1710.05941"},{"key":"e_1_3_2_23_2","doi-asserted-by":"crossref","unstructured":"Muhammad Saqib Saeed Anwar Abbas Anwar Michael Blumenstein et\u00a0al. 2020. COVID-19 detection from radiographs: Is Deep Learning able to handle the crisis?","DOI":"10.36227\/techrxiv.12476426.v1"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"e_1_3_2_25_2","article-title":"Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19","author":"Shi Feng","year":"2020","unstructured":"Feng Shi, Jun Wang, Jun Shi, Ziyan Wu, Qian Wang, Zhenyu Tang, Kelei He, Yinghuan Shi, and Dinggang Shen. 2020. Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19. IEEE Reviews in Biomedical Engineering (2020).","journal-title":"IEEE Reviews in Biomedical Engineering"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2528162"},{"key":"e_1_3_2_27_2","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).","journal-title":"arXiv preprint arXiv:1409.1556"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"e_1_3_2_30_2","first-page":"1","article-title":"A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)","author":"Wang Shuai","year":"2021","unstructured":"Shuai Wang, Bo Kang, Jinlu Ma, Xianjun Zeng, Mingming Xiao, Jia Guo, et\u00a0al. 2021. A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19). European Radiology (2021), 1\u20139.","journal-title":"European Radiology"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2020.11.005"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2020.04.010"},{"key":"e_1_3_2_33_2","article-title":"A seven-layer convolutional neural network for chest CT based COVID-19 diagnosis using stochastic pooling","author":"Zhang Yu-Dong","year":"2020","unstructured":"Yu-Dong Zhang, Suresh Chandra Satapathy, Li-Yao Zhu, Juan Manuel G\u00f3rriz, and Shui-Hua Wang. 2020. A seven-layer convolutional neural network for chest CT based COVID-19 diagnosis using stochastic pooling. IEEE Sensors Journal (2020).","journal-title":"IEEE Sensors Journal"},{"key":"e_1_3_2_34_2","article-title":"COVID-CT-dataset: A CT scan dataset about COVID-19","volume":"490","author":"Zhao Jinyu","year":"2020","unstructured":"Jinyu Zhao, Yichen Zhang, Xuehai He, and Pengtao Xie. 2020. COVID-CT-dataset: A CT scan dataset about COVID-19. 490 (2020). arXiv preprint arXiv:2003.13865.","journal-title":"arXiv preprint arXiv:2003.13865"}],"container-title":["ACM Transactions on Management Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3551647","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3551647","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:00:26Z","timestamp":1750186826000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3551647"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,16]]},"references-count":33,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,3,31]]}},"alternative-id":["10.1145\/3551647"],"URL":"https:\/\/doi.org\/10.1145\/3551647","relation":{},"ISSN":["2158-656X","2158-6578"],"issn-type":[{"value":"2158-656X","type":"print"},{"value":"2158-6578","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,16]]},"assertion":[{"value":"2021-06-04","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-07-13","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-01-16","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}