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Although related studies have demonstrated the high efficiency of deep learning models in diagnosing mpox, key aspects such as model inference speed and parameter size have always been overlooked. Herein, an ultrafast and ultralight network named Fast\u2010MpoxNet is proposed. Fast\u2010MpoxNet, with only 0.27\u2009<jats:sc>m<\/jats:sc> parameters, can process input images at 68\u2009frames per second (FPS) on the CPU. To detect subtle image differences and optimize model parameters better, Fast\u2010MpoxNet incorporates an attention\u2010based feature fusion module and a multiple auxiliary losses enhancement strategy. Experimental results indicate that Fast\u2010MpoxNet, utilizing transfer learning and data augmentation, produces 98.40% classification accuracy for four classes on the mpox dataset. Furthermore, its Recall for early\u2010stage mpox is 93.65%. Most importantly, an application system named Mpox\u2010AISM V2 is developed, suitable for both personal computers and smartphones. Mpox\u2010AISM V2 can rapidly and accurately diagnose mpox and can be easily deployed in various scenarios to offer the public real\u2010time mpox diagnosis services. This work has the potential to mitigate future mpox outbreaks and pave the way for developing real\u2010time diagnostic tools in the healthcare field.<\/jats:p>","DOI":"10.1002\/aisy.202300637","type":"journal-article","created":{"date-parts":[[2024,6,10]],"date-time":"2024-06-10T10:24:00Z","timestamp":1718015040000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Ultrafast\u2010and\u2010Ultralight ConvNet\u2010Based Intelligent Monitoring System for Diagnosing Early\u2010Stage Mpox Anytime and Anywhere"],"prefix":"10.1002","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6348-555X","authenticated-orcid":false,"given":"Yubiao","family":"Yue","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering Guangzhou Medical University  Guangzhou 511436 China"}]},{"given":"Xiaoqiang","family":"Shi","sequence":"additional","affiliation":[{"name":"Shenyang Institute of Computing Technology Chinese Academy of Sciences  Shenyang 110168 China"}]},{"given":"Li","family":"Qin","sequence":"additional","affiliation":[{"name":"Shenyang Institute of Computing Technology Chinese Academy of Sciences  Shenyang 110168 China"}]},{"given":"Xinyue","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering Guangzhou Medical University  Guangzhou 511436 China"}]},{"given":"Jialong","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering Guangzhou Medical University  Guangzhou 511436 China"}]},{"given":"Zipei","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Mathematics and Systems Science Guangdong Polytechnic Normal University  Guangzhou 510665 China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5504-7197","authenticated-orcid":false,"given":"Zhenzhang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mathematics and Systems Science Guangdong Polytechnic Normal University  Guangzhou 510665 China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9544-5840","authenticated-orcid":false,"given":"Yang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering Guangzhou Medical University  Guangzhou 511436 China"}]}],"member":"311","published-online":{"date-parts":[[2024,6,10]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1473-3099(03)00856-9"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/tropicalmed1010008"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/cid\/cit703"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmv.27902"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmii.2022.07.004"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.virs.2022.07.006"},{"key":"e_1_2_10_8_1","unstructured":"Mpox in the U.S.https:\/\/www.cdc.gov\/poxvirus\/mpox\/index.html(accessed: May 2023)."},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2912200"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/7593750"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.oret.2018.10.014"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"e_1_2_10_13_1","doi-asserted-by":"crossref","unstructured":"Y.Dai F.Gieseke S.Oehmcke Y.Wu K.Barnard in2021 IEEE Winter Conf. on Applications of Computer Vision (WACV) Virtual Event\/Waikoloa HI USA2021 pp.3559\u20133568.","DOI":"10.1109\/WACV48630.2021.00360"},{"key":"e_1_2_10_14_1","unstructured":"G.Ghiasi T.\u2010Y.Lin Q. 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