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

Abstract

The quick spread of coronavirus disease 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.

An Analysis of Image Features Extracted by CNNs to Design Classification Models for COVID-19 and Non-COVID-19

“…The framework proposed using the model with EB0 and SVC RBF a-chieved similar performance to those obtained by the works and reported in [26,[90][91][92]. In [90] a crossvalidation with ten folds was used, as well as this work, and applied a method based on generative adversarial network (GAN), obtaining 0.9879 of F1 score. The other works used different methods to analyse the data obtained in relation to the considerations applied in this work.…”

Classifier Fusion for Detection of COVID-19 from CT Scans

“…Goel et al [ 7 ] designed an automated method for SARS-COV-2 detection using a framework that employs Generative Adversarial Network (GAN) for augmentation, Whale optimization for hyperparameter tuning of GAN network, and classification using transfer learned Inception V3 model. The researchers achieved a prediction accuracy value of 99.22% on the benchmark CT scan dataset using train test splits as 7:3.…”

Deep Learning–Based Approaches to Improve Classification Parameters for Diagnosing COVID-19 from CT Images

“…The results of the experiments for COVID-19/non-COVID-19 and COVID-19 pneumonia/other pneumonia classifications are shared in Tables 6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,and 31. In this section, the results are evaluated.…”