基于移动系统的新型冠状病毒识别改进卷积网络

2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2021-11-24 DOI:10.1109/SNPD51163.2021.9705004

Jzau-Sheng Lin, Fang Shen An, Li Cheng Ze

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引用次数: 0

摘要

在本文中，我们改进了一种基于MobileNet的低成本、快速的胸部x射线检测方法。由于MobileNet是一个轻量级的神经网络，我们修改和优化了反向传播学习来训练模型。在随后的COVID-19、肺炎和正常测试中，识别准确率达到99.14%，大大提高了模型的性能。我们的方案可以产生一个适用于低性能移动设备的有效模型。

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Modified Convolutional Network for the Identification of Covid-19 with a Mobile System

In this paper, we modified a low-cost and rapid method to detect chest X-rays based on MobileNet. Because MobileNet is a lightweight neural network, we modified and optimized backpropagation learning to train the model. In the subsequent COVID-19, pneumonia, and normal tests, the recognition accuracy reached 99.14%, which greatly improved the performance of the model. Our scheme can produce an effective model suitable for low-performance mobile devices.

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来源期刊

2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)

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