abstract
- The Coronavirus disease 2019 (COVID-19) pandemic has presented unprecedented challenges to global health-care systems, urgently calling for innovative diagnostic solutions. This paper introduces the Fully Automatic Detection of Covid-19 cases in medical Images of the Lung (FADCIL) system, a cutting-edge deep learning framework designed for rapid and accurate COVID-19 diagnosis from chest computed tomography (CT) images. By leveraging an architecture based on YOLO and 3D U-Net, FADCIL excels in identifying and quantifying lung injuries attributable to COVID-19, distinguishing them from other pathologies. In real-world clinical environments, FADCIL achieves a DICE coefficient above 0.82, highlighting its robust performance and clinical relevance. FADCIL also enhances the reliability of COVID-19 assessment, empowering healthcare professionals to make informed decisions and effectively manage patient care. Thus, this paper outlines the FADCIL architecture and presents an in-depth analysis of quantitative and qualitative evaluation results derived from a novel dataset comprising over 1000 CT scans. Furthermore, we provide access to the FADCIL's source code for public use. © 2024 IEEE.