Beyond the Norm: A Modified VGG-16 Model for COVID-19 Detection

The outbreak of Coronavirus Disease 2019 (COVID-19) in the initial days of December 2019 has severely harmed human health and the world's overall condition. There are currently five million instances that have been confirmed, and the unique virus is continuing spreading quickly throughout the entire world. The manual Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test is time-consuming and difficult, and many hospitals throughout the world do not yet have an adequate number of testing kits. Designing an automated and early diagnosis system that can deliver quick decisions and significantly lower diagnosis error is therefore crucial. Recent advances in emerging Deep Learning (DL) algorithms and emerging Artificial Intelligence (AI) approaches have made the chest X-ray images a viable option for early COVID-19 screening. For visual image analysis, CNNs are the most often utilized class of deep learning neural networks. At the core of CNN is a multi-layered neural network that offers solutions, particularly for the analysis, classification, and recognition of videos and images. This paper proposes a modified VGG-16 model for detection of COVID-19 infection from chest X-ray images. The analysis has been made among the model by considering some important parameters such as accuracy, precision and recall. The model has been validated on publicly available chest X-ray images. The best performance is obtained by the proposed model with an accuracy of 97.94%.