A comparison of deep learning models for detecting COVID-19 in chest X-ray images

Abstract:

COVID-19 has spread around the world rapidly causing a pandemic. In this research, a set of Deep Learning architectures, for diagnosing the presence or not of the disease have been designed and compared; such as, a CNN with 4 incremental convolutional blocks; a VGG-19 architecture; an Inception network; and, a compact CNN model known as MobileNet. For the analysis and comparison, transfer learning techniques were used in forty-five different experiments. All four models were designed to perform binary classification, reaching an accuracy above 95%. A set of different scores were implemented to compare the performance of all models, showing that the VGG-19 and Inception configurations performed the best.

Año de publicación:

2021

Keywords:

• Transfer learning
• Convolutional neural network
• covid-19
• X-Ray Image Analysis
• Machine learning

Fuente:

scopus