Classification of COVID-19 and Influenza Patients Using Deep Learning.
Classification of COVID-19 and Influenza Patients Using Deep Learning.
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DOI：
10.1155/2022/8549707
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发表时间：
2022
影响因子：
--
通讯作者：
Iqbal Z
中科院分区：
文献类型：
--
作者：
Aftab M;Amin R;Koundal D;Aldabbas H;Alouffi B;Iqbal Z
冠状病毒（COVID-19）是一种致命的病毒，最初以流感样症状开始。COVID-19在中国出现并迅速蔓延至地球仪，导致2019-22年的冠状病毒疫情。由于这种病毒在早期阶段与流感非常相似，因此其准确检测具有挑战性。目前正在开发几种早期检测病毒的技术。深度学习技术是检测各种疾病的方便工具。对于COVID-19和流感的分类，我们提出了量身定制的深度学习模型。一个公开的X射线图像数据集被用来开发拟议的模型。根据测试结果，深度学习模型可以准确诊断正常、流感和COVID-19病例。我们提出的长短期记忆（LSTM）技术在胸部X射线图像的评估阶段优于CNN模型，准确率达到98%。
Coronavirus (COVID-19) is a deadly virus that initially starts with flu-like symptoms. COVID-19 emerged in China and quickly spread around the globe, resulting in the coronavirus epidemic of 2019–22. As this virus is very similar to influenza in its early stages, its accurate detection is challenging. Several techniques for detecting the virus in its early stages are being developed. Deep learning techniques are a handy tool for detecting various diseases. For the classification of COVID-19 and influenza, we proposed tailored deep learning models. A publicly available dataset of X-ray images was used to develop proposed models. According to test results, deep learning models can accurately diagnose normal, influenza, and COVID-19 cases. Our proposed long short-term memory (LSTM) technique outperformed the CNN model in the evaluation phase on chest X-ray images, achieving 98% accuracy.
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影响因子：
2.5
作者：
Grech, Victor;Borg, Michael
通讯作者：
Borg, Michael
影响因子：
168.9
作者：
Cox, NJ;Subbarao, K
通讯作者：
Subbarao, K
DOI：
10.1016/j.isprsjprs.2019.01.015
发表时间：
2019-03-01
影响因子：
12.7
作者：
Hua, Yuansheng;Mou, Lichao;Zhu, Xiao Xiang
通讯作者：
Zhu, Xiao Xiang
影响因子：
3.8
作者：
Luzi, Livio;Radaelli, Maria Grazia
通讯作者：
Radaelli, Maria Grazia
DOI：
10.2807/1560-7917.es.2020.25.47.2001943
发表时间：
2020-11
期刊：
Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin
影响因子：
--
作者：
Simões D;Stengaard AR;Combs L;Raben D;EuroTEST COVID-19 impact assessment consortium of partners
通讯作者：
EuroTEST COVID-19 impact assessment consortium of partners