Abstract
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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Data Availability
The data in the experiments, used to support the findings of this study are available from the corresponding author upon request.
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Acknowledgment
This work has been supported in part by the Natural Science Foundation of China under grant No. 62262030, the Science and Technology Research Project of Jiangxi Provincial Department of Education (No. GJJ170234).
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Zhong, T., Wen, H., Cao, Z., Zou, X., Tang, Q., Wang, W. (2023). Pneumonia Image Recognition Based on Transfer Learning. In: Cao, Y., Shao, X. (eds) Mobile Networks and Management. MONAMI 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 474. Springer, Cham. https://doi.org/10.1007/978-3-031-32443-7_8
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DOI: https://doi.org/10.1007/978-3-031-32443-7_8
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