Optimized Residual Attention Based Generalized Adversarial Network for COVID-19 Classification Using Chest CT Images

The early detection and classification of COVID-19 is crucial for disease diagnosis and control. To reduce the need for medical professionals, fast and accurate detection approaches for COVID-19 are required. Due to environmental concerns, the quality of the image gets degraded. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more trustworthy, useful, and rapid technique to classify and evaluate COVID-19. Thus, the performance of the deep learning (DL) techniques is diminished. Therefore, a CT image-based hybrid DL technology is presented in this article for the classification of COVID-19 disease as COVID or non-COVID or pneumonia. Initially, in the pre-processing stage, the hybrid nonlocal moment bilateral filtering (Hybrid NMBF) technique is introduced for image de-noising and re-sizing. After pre-processing, the image is fed into the feature extraction phase. Gray-level covariance matrices (GLCM) technique is used to extract the relevant features and reduce feature dimensionality issues. For the feature selection process, the enhanced Archimedes optimization algorithm (EAOA) is introduced to select optimal features. The residual channel attention-generative adversarial network (RCA-GAN) technique is introduced for image classification. Here, the hyperparameter of the network is tuned using the Sandpiper optimization (SPO) algorithm to optimize the loss function. The data set used in this research is COVID-CT-machine learning deep learning (MD), and the performance is analyzed using the MATLAB tool. In the experimental scenario, the proposed system achieves 98.3% accuracy, 98.7% specificity, 99.4% sensitivity, 97.4% F-score, and 96.1% kappa. The attained results prove that the proposed system works better than the traditional techniques.