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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2021,10,31]]},"abstract":"<jats:p>With the rapid development of Artificial Intelligence (AI), deep learning has increasingly become a research hotspot in various fields, such as medical image classification. Traditional deep learning models use Bilinear Interpolation when processing classification tasks of multi-size medical image dataset, which will cause the loss of information of the image, and then affect the classification effect. In response to this problem, this work proposes a solution for an adaptive size deep learning model. First, according to the characteristics of the multi-size medical image dataset, the optimal size set module is proposed in combination with the unpooling process. Next, an adaptive deep learning model module is proposed based on the existing deep learning model. Then, the model is fused with the size fine-tuning module used to process multi-size medical images to obtain a solution of the adaptive size deep learning model. Finally, the proposed solution model is applied to the pneumonia CT medical image dataset. Through experiments, it can be seen that the model has strong robustness, and the classification effect is improved by about 4% compared with traditional algorithms.<\/jats:p>","DOI":"10.1145\/3465220","type":"journal-article","created":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T15:46:12Z","timestamp":1635263172000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":24,"title":["Medical Image Classification based on an Adaptive Size Deep Learning Model"],"prefix":"10.1145","volume":"17","author":[{"given":"Xiangbin","family":"Liu","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Hunan Normal University, China and Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiesheng","family":"He","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Hunan Normal University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liping","family":"Song","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Hunan Normal University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuai","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Hunan Normal University, China, Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, China, and Hunan Xiangjiang Artificial Intelligence Academy, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gautam","family":"Srivastava","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, Brandon University, Brandon, Canada and Research Centre for Interneural Computing, China Medical University, Taichung, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,10,26]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICTKE.2017.8259629"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2019.05.039"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.02.056"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymeth.2019.04.008"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.2174\/1573394711666150827203543"},{"key":"e_1_3_1_7_2","doi-asserted-by":"crossref","unstructured":"Yu-Dong Zhang Zhengchao Dong Shui-Hua Wang Xiang Yu Xujing Yao Qinghua Zhou Hua Hu Min Li Carmen Jim\u00e9nez-Mesa Javier Ramirez Francisco J. 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