论文标题
Ecovnet：基于有效网络的深卷卷神经网络的合奏，可检测胸部X射线的COVID-19
ECOVNet: An Ensemble of Deep Convolutional Neural Networks Based on EfficientNet to Detect COVID-19 From Chest X-rays
论文作者
论文摘要
本文提出了一个基于ExtificedNet的深卷积神经网络（CNN）的合奏，使用大型胸部X射线数据集检测COVID-19。首先，开放式大型胸部X射线收藏集得到了增强，然后将ImageNet预训练的高效网重转移，并使用一些经过训练的定制微调顶层，然后进行模型快照集合，以对与胸部X射线进行分类，并对与COVID-19，正常和pneumonia相对应。模型快照的预测是在单个训练期间创建的，它是通过两种整体策略组合在一起的，即硬合奏和软集合，以减轻分类性能和对分类胸部X射线分类的相关任务。
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.
