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ECOVNet: An Ensemble of Deep Convolutional Neural Networks Based on EfficientNet to Detect COVID-19 From Chest X-rays
arXiv - CS - Computer Vision and Pattern Recognition Pub Date : 2020-09-24 , DOI: arxiv-2009.11850 Nihad Karim Chowdhury, Muhammad Ashad Kabir, Md. Muhtadir Rahman, Noortaz Rezoana
arXiv - CS - Computer Vision and Pattern Recognition Pub Date : 2020-09-24 , DOI: arxiv-2009.11850 Nihad Karim Chowdhury, Muhammad Ashad Kabir, Md. Muhtadir Rahman, Noortaz Rezoana
This paper proposed an ensemble of deep convolutional neural networks (CNN)
based on EfficientNet, named ECOVNet, to detect COVID-19 using a large chest
X-ray data set. At first, the open-access large chest X-ray collection is
augmented, and then ImageNet pre-trained weights for EfficientNet is
transferred with some customized fine-tuning top layers that are trained,
followed by an ensemble of model snapshots to classify chest X-rays
corresponding to COVID-19, normal, and pneumonia. The predictions of the model
snapshots, which are created during a single training, are combined through two
ensemble strategies, i.e., hard ensemble and soft ensemble to ameliorate
classification performance and generalization in the related task of
classifying chest X-rays.
中文翻译:
ECOVNet:基于 EfficientNet 的深度卷积神经网络集合,可从胸部 X 射线检测 COVID-19
本文提出了一个基于 EfficientNet 的深度卷积神经网络 (CNN) 集成,名为 ECOVNet,以使用大型胸部 X 射线数据集检测 COVID-19。首先,增加开放访问的大型胸部 X 射线集合,然后将用于 EfficientNet 的 ImageNet 预训练权重与一些经过训练的定制微调顶层进行传输,然后是一组模型快照以对胸部 X 进行分类- 对应于 COVID-19、正常和肺炎的射线。在单次训练期间创建的模型快照的预测通过两种集成策略(即硬集成和软集成)进行组合,以改善分类性能和分类胸部 X 射线相关任务中的泛化。
更新日期:2020-10-19
中文翻译:
ECOVNet:基于 EfficientNet 的深度卷积神经网络集合,可从胸部 X 射线检测 COVID-19
本文提出了一个基于 EfficientNet 的深度卷积神经网络 (CNN) 集成,名为 ECOVNet,以使用大型胸部 X 射线数据集检测 COVID-19。首先,增加开放访问的大型胸部 X 射线集合,然后将用于 EfficientNet 的 ImageNet 预训练权重与一些经过训练的定制微调顶层进行传输,然后是一组模型快照以对胸部 X 进行分类- 对应于 COVID-19、正常和肺炎的射线。在单次训练期间创建的模型快照的预测通过两种集成策略(即硬集成和软集成)进行组合,以改善分类性能和分类胸部 X 射线相关任务中的泛化。