Evaluation of Scalability and Degree of Fine-Tuning of Deep Convolutional Neural Networks for COVID-19 Screening on Chest X-ray Images Using Explainable Deep-Learning Algorithm.

Ki-Sun Lee,Won Suk Choi,Nan Hee Kim,Eun-Tae Jeon,Ki Yeol Lee,Jae Young Kim

doi:10.3390/jpm10040213

Abstract

According to recent studies, patients with COVID-19 have different feature characteristics on chest X-ray (CXR) than those with other lung diseases. This study aimed at evaluating the layer depths and degree of fine-tuning on transfer learning with a deep convolutional neural network (CNN)-based COVID-19 screening in CXR to identify efficient transfer learning strategies. The CXR images used in this study were collected from publicly available repositories, and the collected images were classified into three classes: COVID-19, pneumonia, and normal. To evaluate the effect of layer depths of the same CNN architecture, CNNs called VGG-16 and VGG-19 were used as backbone networks. Then, each backbone network was trained with different degrees of fine-tuning and comparatively evaluated. The experimental results showed the highest AUC value to be 0.950 concerning COVID-19 classification in the experimental group of a fine-tuned with only 2/5 blocks of the VGG16 backbone network. In conclusion, in the classification of medical images with a limited number of data, a deeper layer depth may not guarantee better results. In addition, even if the same pre-trained CNN architecture is used, an appropriate degree of fine-tuning can help to build an efficient deep learning model.

Highlights

CORONAVIRUS disease (COVID-19) has quickly become a global pandemic since it was first reported in December 2019, reaching approximately 21.3 million confirmed cases and 761,799 deaths as of 16 August 2020 [1]
According to the reports on chest X-ray (CXR) characteristics of patients confirmed as the COVID-19 case, it demonstrated multi-lobar involvement and peripheral airspace opacities, which was most frequently demonstrated as ground-glass [6]
By employing the gradient-weighted class activation map (Grad-CAM) [18,19], this study provided a visual interpretation explaining the feature characteristic region that the deep learning (DL) model has the most influence on classification prediction

Summary

Introduction

CORONAVIRUS disease (COVID-19) has quickly become a global pandemic since it was first reported in December 2019, reaching approximately 21.3 million confirmed cases and 761,799 deaths as of 16 August 2020 [1]. Due to the highly infectious nature and unavailability of appropriate treatments and vaccines for the virus, early screening of COVID-19 is crucial to prevent the spread of the disease by the timely isolation of susceptive individuals and the proper allocation of limited medical resources. According to the reports on CXR characteristics of patients confirmed as the COVID-19 case, it demonstrated multi-lobar involvement and peripheral airspace opacities, which was most frequently demonstrated as ground-glass [6]. In the early stages of COVID-19, this ground-glass pattern may appear at the edges of the lung vessels, or as asymmetric diffused airspace opacities [7], it can be difficult to visually detect the characteristic patterns of COVID-19 from X-rays. Considering the fact that the number of suspected patients increases exponentially in contrast to the limited number of highly trained radiologists, the diagnostic supporting procedures, using an automated screening algorithm with a producing objective, reproducible, and scalable results, can speed up earlier precise diagnosis

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