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

Authors: Eliú Moreno-Ramírez; Héctor Anaya-Sánchez; José Fco. Martínez-Trinidad; J. Ariel Carrasco-Ochoa

Addresses: Ciencias Computacionales, Instituto Nacional de Astrofísica Óptica y Electrónica, Luis Enrique Erro #1, Sta María Tonanzintla, Cholula, 72840, Puebla, Mexico ' Ciencias Computacionales, Instituto Nacional de Astrofísica Óptica y Electrónica, Luis Enrique Erro #1, Sta María Tonanzintla, Cholula, 72840, Puebla, Mexico ' Ciencias Computacionales, Instituto Nacional de Astrofísica Óptica y Electrónica, Luis Enrique Erro #1, Sta María Tonanzintla, Cholula, 72840, Puebla, Mexico ' Ciencias Computacionales, Instituto Nacional de Astrofísica Óptica y Electrónica, Luis Enrique Erro #1, Sta María Tonanzintla, Cholula, 72840, Puebla, Mexico

Abstract: 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.

Keywords: COVID-19 classification; deep learning; machine learning; transfer learning.

DOI: 10.1504/IJAPR.2024.146815

International Journal of Applied Pattern Recognition, 2024 Vol.7 No.3/4, pp.205 - 221

Received: 13 Sep 2024
Accepted: 10 Apr 2025
Published online: 19 Jun 2025 *