Detection of COVID-19 Using Denoising Autoencoders and Gabor Filters

ABSTRACT

As of 2019, COVID-19 is the most difficult issue that we are facing. Till now, it has reached over 30 million deaths. Since SARS-CoV-2 is the new virus, it took time to investigate and examine the influence of Coronavirus in human. After analyzing the spreading and infection of COVID-19, researchers applied Artificial Intelligence (AI) techniques to detect COVID-19 quickly to balance the rapid spreading of the virus. Image segmentation is a critical first step in clinical implementations, is a vital role in computer - aided diagnosis that relies heavily on image recognition. Image segmentation is used in medical MRI research to determine the proportions of different anatomical areas of the tissue, as well as how they change as the disease progresses. CT scans are often used to aid with diagnoses. Computer-assisted therapy (CAD) using AI is a particularly significant research area in intelligent healthcare. This paper presents the detection of COVID-19 at an early stage using autoencoders algorithm and Generative Adversarial Networks (GAN) using deep learning approach with more accurate results. The images of Chest Radiograph (CRG) and Chest Computed Tomography (CCT) are used as a trained dataset to detect since SARS-CoV-2 first affect the respiratory system in humans. We achieved a ratio of 1.0, 0.99, and 0.96, the combined dataset was randomly divided into the train, validation, and test sets. Although the early detection of Coronavirus is still a question since the accuracy of the deep learning approach is still under research. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.