ECOVNet: An Ensemble of Deep Convolutional Neural Networks Based on EfficientNet to Detect COVID-19 From Chest X-rays
This addresses the problem of automated COVID-19 diagnosis from medical images for healthcare applications, but it is incremental as it builds on existing EfficientNet and ensemble methods.
The paper tackled detecting COVID-19 from chest X-rays by proposing ECOVNet, an ensemble of EfficientNet-based CNNs, achieving improved classification performance and generalization for COVID-19, normal, and pneumonia cases.
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.