Transfer Learning-Based Automatic Detection of Coronavirus Disease 2019 (COVID-19) from Chest X-ray Images

Abstract

Background: Coronavirus disease 2019 (COVID-19) is an emerging infectious disease and global health crisis. Although real-time reverse transcription polymerase chain reaction (RT-PCR) is known as the most widely laboratory method to detect the COVID-19 from respiratory specimens. It suffers from several main drawbacks such as time-consuming, high false-negative results, and limited availability. Therefore, the automatically detect of COVID-19 will be required. Objective: This study aimed to use an automated deep convolution neural network based pre-trained transfer models for detection of COVID-19 infection in chest X-rays. Material and Methods: In a retrospective study, we have applied Visual Geometry Group (VGG)-16, VGG-19, MobileNet, and InceptionResNetV2 pre-trained models for detection COVID-19 infection from 348 chest X-ray images. Results: Our proposed models have been trained and tested on a dataset which previously prepared. The all proposed models provide accuracy greater than 90.0%. The pre-trained MobileNet model provides the highest classification performance of automated COVID-19 classification with 99.1% accuracy in comparison with other three proposed models. The plotted area under curve (AUC) of receiver operating characteristics (ROC) of VGG16, VGG19, MobileNet, and InceptionResNetV2 models are 0.92, 0.91, 0.99, and 0.97, respectively. Conclusion: The all proposed models were able to perform binary classification with the accuracy more than 90.0% for COVID-19 diagnosis. Our data indicated that the MobileNet can be considered as a promising model to detect COVID-19 cases. In the future, by increasing the number of samples of COVID-19 chest X-rays to the training dataset, the accuracy and robustness of our proposed models increase further.

X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic

“…Based on the used X-ray datasets, several studies differentiated the data into two classes of patients with COVID-19 and non-COVID-19 patients [ 21 , 24 , 25 , 29 , 36 , 39 , 40 , 42 , 45 , 46 ]. In others, the database included more than two classes, e.g., viral pneumonia, bacterial pneumonia, and normal and COVID-19 cases [ 17 , 23 , 30 – 35 , 37 , 39 , 41 , 43 , 47 – 50 ].…”

“…In these studies, the main purpose is to use early chest X-ray results to identify infection cases from other suspicious or normal cases. Twenty-eight studies aiming for detection used a spectrum of ML techniques for COVID-19 identification by X-ray image analysis [ 21 , 22 , 25 , 27 – 49 ]. Another term that is very similar in function to detection is diagnosis .…”

“…Oversampling augmentations create synthetic instances and add them to the training set. Due to the limited data on COVID-19, almost all studies have used the data augmentation technique [ 25 , 30 , 31 , 43 , 45 , 64 , 89 – 91 ].…”

Deep Learning Approach for Analyzing the COVID-19 Chest X-Rays

“…have also classified chest X-Ray into two categories using four transfer learning models, in a result VGG16 provided an accuracy of 93.6 as compared to 90.8% provided by VGG19. [ 30 ]…”

Deep learning applied to automatic disease detection using chest X‐rays

“…Papers examined classification with classes including COVID‐19 pneumonia, viral pneumonia, bacterial pneumonia and normal. Performance of the models ranged from an accuracy of 89.6%‐98.69% for 4‐class classification, 93.5%‐99.62% for 3‐class classification and 89.3%‐99.16% for 2‐class classification 44‐54 . A comparison of the performance of pretrained DNN models, using transfer learning in the detection of COVID‐19 pneumonia, is shown in Table 5.…”