Transfer Learning to Detect COVID-19 Automatically from X-Ray Images Using Convolutional Neural Networks.

Mundher Mohammed Taresh,Ningbo Zhu,Talal Ahmed Ali Ali,Asaad Shakir Hameed,Modhi Lafta Mutar,Jyh-Cheng Chen

doi:10.1155/2021/8828404

Abstract

The novel coronavirus disease 2019 (COVID-19) is a contagious disease that has caused thousands of deaths and infected millions worldwide. Thus, various technologies that allow for the fast detection of COVID-19 infections with high accuracy can offer healthcare professionals much-needed help. This study is aimed at evaluating the effectiveness of the state-of-the-art pretrained Convolutional Neural Networks (CNNs) on the automatic diagnosis of COVID-19 from chest X-rays (CXRs). The dataset used in the experiments consists of 1200 CXR images from individuals with COVID-19, 1345 CXR images from individuals with viral pneumonia, and 1341 CXR images from healthy individuals. In this paper, the effectiveness of artificial intelligence (AI) in the rapid and precise identification of COVID-19 from CXR images has been explored based on different pretrained deep learning algorithms and fine-tuned to maximise detection accuracy to identify the best algorithms. The results showed that deep learning with X-ray imaging is useful in collecting critical biological markers associated with COVID-19 infections. VGG16 and MobileNet obtained the highest accuracy of 98.28%. However, VGG16 outperformed all other models in COVID-19 detection with an accuracy, F1 score, precision, specificity, and sensitivity of 98.72%, 97.59%, 96.43%, 98.70%, and 98.78%, respectively. The outstanding performance of these pretrained models can significantly improve the speed and accuracy of COVID-19 diagnosis. However, a larger dataset of COVID-19 X-ray images is required for a more accurate and reliable identification of COVID-19 infections when using deep transfer learning. This would be extremely beneficial in this pandemic when the disease burden and the need for preventive measures are in conflict with the currently available resources.

Highlights

The novel coronavirus disease 2019 (COVID-19) pandemic remains a worldwide concern, threatening to devastate global health
Beforehand, we conducted many experiments on a large chest X-ray dataset of COVID-19 samples fed into eight well-known pretrained Convolutional Neural Networks (CNNs) models, namely, InceptionV3, Xception, InceptionResNetV2, MobileNet, VGG16, DenseNet169, NasNetLarge, and DenseNet121, each with different selections of frozen layers and number of trainable convolution blocks
While there was only a marginal difference between the results of the various CNNs, VGG16 and MobileNet obtained the highest accuracy of 98.29%

Summary

Introduction

The novel coronavirus disease 2019 (COVID-19) pandemic remains a worldwide concern, threatening to devastate global health. The dramatic proliferation of COVID-19 has resulted in an insufficient number of laboratory kits, creating a significant challenge [3]. The use of radiological examinations in identifying infections has become increasingly attractive during the COVID-19 outbreak [4]. Computed tomography (CT) scans have proven to be more effective, the increasing number of patients and the consequent rise in radiological examinations are making it impossible to continuously rely on chest CT scans for each individual patient from diagnosis to discharge. A high reliance on CT scans will impose a significant burden on radiology departments, rendering chest X-rays (CXRs) a more feasible option for COVID-19 detection [5]

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