ECOVNet: An Ensemble of Deep Convolutional Neural Networks Based on EfficientNet to Detect COVID-19 From Chest X-rays

TL;DR

This paper introduces ECOVNet, an ensemble of EfficientNet-based CNNs, designed to improve COVID-19 detection from chest X-rays by leveraging data augmentation, transfer learning, and snapshot ensembling strategies.

Contribution

It presents a novel ensemble approach combining EfficientNet models with snapshot ensembling and data augmentation for enhanced COVID-19 detection accuracy.

Findings

Improved classification accuracy over single models

Effective generalization through ensemble strategies

Robust detection of COVID-19, pneumonia, and normal cases

Abstract

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.