COVIVISION: A DEEP LEARNING-POWERED GUI FRAMEWORK FOR RAPID COVID-19 DIAGNOSTICS

Abstract

The COVID-19 pandemic has emphasized the need for fast, reliable, and accessible diagnostic tools to support healthcare systems worldwide. Traditional testing methods, while accurate, often suffer from limitations such as time delays, high costs, and dependency on laboratory infrastructure. To address these challenges, this study introduces CoviVision, a deep learning-powered graphical user interface (GUI) framework designed to provide rapid and accurate COVID-19 diagnostics. The proposed system leverages convolutional neural networks (CNNs) trained on chest X-ray and CT scan datasets to identify infection patterns with high precision. By integrating the deep learning model into an interactive GUI, the framework ensures usability for healthcare professionals with minimal technical expertise, enabling realtime data input, visualization, and diagnostic results. Experimental evaluations demonstrate that CoviVision achieves superior accuracy, sensitivity, and specificity compared to conventional methods, thereby supporting early detection and effective decision-making. This research highlights the potential of combining deep learning with user-friendly interfaces to democratize diagnostic technology and enhance the responsiveness of healthcare systems in pandemic scenarios.