文献类型：期刊文献

英文题名：Multi-MedVit: a deep learning approach for the diagnosis of COVID-19 with the CT images

作者：Cai, Yunjie[1,2]; Zheng, Zeqi[1]; Nie, Shanling[1]; Guo, Yue[1]; Zhang, Ruijie[3]; Yang, Hai[1]

机构：[1] East China University of Science and Technology, Department of Computer Science and Engineering, Shanghai, 200237, China; [2] Shanghai Key Laboratory of Computer Software Evaluating and Testing, Shanghai, China; [3] University of Southampton, School of Electronics and Computer Science, United Kingdom

年份：2022

起止页码：2247

外文期刊名：Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

收录：EI(收录号：20230413452404)

语种：英文

外文关键词：Computerized tomography - Deep learning - Image classification - Learning systems

摘要：The grim situation of novel coronavirus pneumonia 2019 (COVID-19) and its terrible spreading speed have already constituted a severe risk to human life, so it is ultimately essential to rapidly and accurately diagnose for COVID-19 pneumonia. Based on this study's 746 lung CT images, we propose Multi-MedVit, a novel auxiliary COVID-19 diagnosis framework based on the multi-input Transformer. We compare Multi-MedVit with state-of-the-art deep learning methods, such as CNN, VGG16, and ResNet50. Multi-MedVit outperformed the other methods on the benchmark dataset and proved that multiscale data input for data augmentation helped enhance model stability. Based on an interpretable analysis of the input and output of Multi-MedVit, we found that with the support of the training set data, the model has been possible to accurately focus on the lesion area for diagnosis of COVID-19 without expert annotations, which can provide initial references containing more potential information to doctors more precisely and fleetly. ? 2022 IEEE.