COVID-19's X-ray images classification: training from scratch or transfer learning?

This paper presents a comprehensive analysis of 11 state-of-the-art deep convolutional neural network (CNN) models for COVID-19's X-ray image classification with the two configurations more studied in the literature: transfer learning with fine-tuning and training from scratch. All models were assessed under the same experimental framework. Unlike other works, we used a dataset compiled from several public datasets, increasing its variability to reduce the risk of overfitting. Our results show which deep convolutional neural networks performed the best in accuracy and F1-score when training from scratch and with transfer learning.