ABSTRACT
The coronavirus disease (COVID-19) makes humans suffer from mild to moderate respiratory problems, with severe cases requiring special treatment. In many severe cases, elderly individuals and people with pre-existing medical issues like lung-related disease, insulin-dependent disease, and carcinoma, are more prone to difficulty breathing and developing a severe illness. To detect the coronavirus here, X-ray radiograph images are considered. The main motive for using X-ray radiograph images is their being cost-effective and being able to give considerable accuracy compared to its counterpart, computed tomography (CT) scans. In this study, the deep learning model Visual Geometry Group (VGG)16 using the transfer learning method and image augmentation techniques was employed for automatic COVID-19 diagnosis. These two techniques will assist the deep learning model to learn the target task by improving the baseline performance by using fewer X-ray radiograph images in the training phase and showing improvements in the model development time by utilising knowledge gained from a source model. Many deep learning methods have been published in the literature to solve the same cases, but the proposed method uses a simple VGG16 model with transfer learning, which takes less processing time and gives satisfactory results even by using fewer training samples. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.