学习迁移
人工智能
计算机科学
肺炎
深度学习
理论(学习稳定性)
2019年冠状病毒病(COVID-19)
机器学习
人工神经网络
模式识别(心理学)
医学
传染病(医学专业)
病理
内科学
疾病
作者
Tao Zhong,Huiting Wen,Zhang Cao,Xinhui Zou,Quanhua Tang,Wenle Wang
出处
期刊:Mobile Networks and Management
日期:2023-01-01
卷期号:: 116-126
标识
DOI:10.1007/978-3-031-32443-7_8
摘要
With the rapid development of artificial intelligence (AI), the anomalies detection in biomedical has became important in patients’ health monitoring. The pneumonia, including COVID-19, is a global threat. Detecting the infected patients in time is very critical to combating this epidemics. Thus, a rapid and accurate method for detecting pneumonia is urgently needed. In this paper, a deep-learning detection model, is designed to detect pneumonia efficient. Since training a neural network needs consuming a lot of time resources and computing resources, transfer learning is used for pre-training. At the same time, in order to improve the detection efficiency, we combine various deep learning models, and then perform prediction and classification. The simulation results show that comparing with the 91.5% accuracy of the traditional CNN model, the transfer learning model consisting of vgg16VGG16, vgg19VGG19, RresNnet50 and Xxecption reached 93.27%, 93.43%, 92.31% and 90.22% respectively. Most of the models are superior to the traditional models and have excellent stability with less time consuming.
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