Automatic Screening of COVID-19 Using an Optimized Generative Adversarial Network

Abstract

The quick spread of coronavirus disease (COVID-19) has resulted in a global pandemic and more than fifteen million confirmed cases. To battle this spread, clinical imaging techniques, for example, computed tomography (CT), can be utilized for diagnosis. Automatic identification software tools are essential for helping to screen COVID-19 using CT images. However, there are few datasets available, making it difficult to train deep learning (DL) networks. To address this issue, a generative adversarial network (GAN) is proposed in this work to generate more CT images. The Whale Optimization Algorithm (WOA) is used to optimize the hyperparameters of GAN's generator. The proposed method is tested and validated with different classification and meta-heuristics algorithms using the SARS-CoV-2 CT-Scan dataset, consisting of COVID-19 and non-COVID-19 images. The performance metrics of the proposed optimized model, including accuracy (99.22%), sensitivity (99.78%), specificity (97.78%), F1-score (98.79%), positive predictive value (97.82%), and negative predictive value (99.77%), as well as its confusion matrix and receiver operating characteristic (ROC) curves, indicate that it performs better than state-of-the-art methods. This proposed model will help in the automatic screening of COVID-19 patients and decrease the burden on medicinal services frameworks.

Keywords: Automatic diagnosis; COVID-19; Coronavirus; Deep learning; Generative Adversarial Network; Whale Optimization Algorithm.

Fig. 1

(a) CT image of a COVID-19-infected person showing ground glass opacities. (b) CT image of a non-COVID-19-infected person [https://www.kaggle.com/plameneduardo/sarscov2-ctscan-dataset]

Fig. 2

General architecture of the generative adversarial network

Fig. 3

Workflow of the proposed methodology

Fig. 4

Sample COVID-19 training images

Fig. 5

Sample non-COVID-19 training images

Fig. 6

Training progress of the InceptionV3 network (a) accuracy and loss (b) results

Fig. 7

Sample COVID-19 testing images

Fig. 8

Sample non-COVID-19 testing images

Fig. 9

CT images generated using the optimized-GAN (a) COVID-19 and (b) non-COVID-19

Fig. 10

Comparison of ROC curves (a) non-optimized GAN and (b) optimized GAN

Fig. 11

Comparison of confusion matrixes (a) non-optimized GAN and (b) optimized GAN

Fig. 12

Comparison of the confusion matrixes

Fig. 13

Comparison of the ROC curves (a) AlexNet, (b) GoogleNet, (c) VGG19, (d) SqueezeNet, (e) ResNet-50, (f) InceptionV3

Fig. 14

Cross-validation results