Deep Learning for The Detection of COVID-19 Using Transfer Learning and Model Integration

Ningwei Wang, Hongzhe Liu, Cheng Xu
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引用次数: 50

Abstract

We researched the diagnostic capabilities of deep learning on chest radiographs and an image classifier based on the COVID-Net was presented to classify chest X-Ray images. In the case of a small amount of COVID-19 data, data enhancement was proposed to expanded COVID-19 data 17 times. Our model aims at transfer learning, model integration and classify chest X-Ray images according to three labels: normal, COVID-19 and viral pneumonia. According to the accuracy and loss value, choose the models ResNet-101 and ResNet-152 with good effect for fusion, and dynamically improve their weight ratio during the training process. After training, the model can achieve 96.1% of the types of chest X-Ray images accuracy on the test set. This technology has higher sensitivity than radiologists in the screening and diagnosis of lung nodules. As an auxiliary diagnostic technology, it can help radiologists improve work efficiency and diagnostic accuracy.
基于迁移学习和模型集成的COVID-19检测深度学习
研究了深度学习对胸片的诊断能力,提出了一种基于COVID-Net的胸片图像分类器。在COVID-19数据量较少的情况下,提出了数据增强,将COVID-19数据扩展了17倍。我们的模型旨在迁移学习,模型整合,并根据正常,COVID-19和病毒性肺炎三个标签对胸部x射线图像进行分类。根据准确率和损失值,选择效果较好的ResNet-101和ResNet-152模型进行融合,并在训练过程中动态提高其权重比。经过训练,该模型在测试集上可以达到96.1%的胸部x射线图像类型准确率。该技术对肺结节的筛查和诊断具有比放射科医师更高的敏感性。作为一种辅助诊断技术,它可以帮助放射科医生提高工作效率和诊断准确性。
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