Classification of COVID-19 chest X-rays with deep learning: new models or fine tuning?

Abstract

Background and objectives: Chest X-ray data have been found to be very promising for assessing COVID-19 patients, especially for resolving emergency-department and urgent-care-center overcapacity. Deep-learning (DL) methods in artificial intelligence (AI) play a dominant role as high-performance classifiers in the detection of the disease using chest X-rays. Given many new DL models have been being developed for this purpose, the objective of this study is to investigate the fine tuning of pretrained convolutional neural networks (CNNs) for the classification of COVID-19 using chest X-rays. If fine-tuned pre-trained CNNs can provide equivalent or better classification results than other more sophisticated CNNs, then the deployment of AI-based tools for detecting COVID-19 using chest X-ray data can be more rapid and cost-effective.

Methods: Three pretrained CNNs, which are AlexNet, GoogleNet, and SqueezeNet, were selected and fine-tuned without data augmentation to carry out 2-class and 3-class classification tasks using 3 public chest X-ray databases.

Results: In comparison with other recently developed DL models, the 3 pretrained CNNs achieved very high classification results in terms of accuracy, sensitivity, specificity, precision, $F_1$ score, and area under the receiver-operating-characteristic curve.

Conclusion: AlexNet, GoogleNet, and SqueezeNet require the least training time among pretrained DL models, but with suitable selection of training parameters, excellent classification results can be achieved without data augmentation by these networks. The findings contribute to the urgent need for harnessing the pandemic by facilitating the deployment of AI tools that are fully automated and readily available in the public domain for rapid implementation.

Keywords: Artificial intelligence; COVID-19; Chest X-rays; Classification; Deep learning.

Fig. 1

Chest X-rays from COVID-19 Radiography Database: COVID-19 (Row 1), viral pneumonia (Row 2), and normal (Row 3)

Fig. 2

Chest X-rays of COVID-19 from the Chest X-Ray Dataset Initiative

Fig. 3

Chest X-rays of COVID-19 from the IEEE8023/Covid Chest X-Ray Dataset

Fig. 4

Transfer-learning processes of the pretrained convolutional neural networks for the 2-class classification using Dataset 1

Fig. 5

Features at the fully connected layers of the pretrained convolutional neural networks for the 2-class classification using Dataset 1: This feature visualization provides insights into the performance of the convolutional neural networks for differentiating COVID-19 (left images) from normal (right images) conditions. The networks first learned simple edges and texture, then more abstract properties of the two classes in higher layers, resulting in distinctive features for effective pattern classification