Abstract
BACKGROUND: The COVID-19 pandemic has caused massive devastation worldwide, and its effects still persist. Managing the early stages was difficult, but scientists worked tirelessly to control it. The emergence of variants continues to pose a threat, raising doubts about the capability of the healthcare system. Healthcare practitioners have faced immense strain under a massive patient load, while delays in testing have caused deaths due to untimely treatment. Moreover, relying only on RT-PCR testing is insufficient because of its diagnostic errors. MATERIALS AND METHODS: To address these challenges, this study introduces a Smart Imaging Lab Framework for hospitals. The approach uses a convolutional neural network (CNN) model to carry out rapid X-ray and CT-scan assessments of emergency patients showing severe symptoms, following RT-PCR testing. In addition, blood tests help determine the severity of infection. Patients in critical condition are transferred to intensive care units, while those with milder cases remain in general wards. RESULTS: The framework uses a 16-layer CNN framework for X-ray and CT-scan imaging, achieving 99.02% and 98.49% accuracy, respectively. Severity assessment with Extra Randomized Trees reached 98.00% accuracy. DISCUSSION: These findings highlight the potential of the system to be adopted in hospitals, enabling regular health monitoring and timely intervention. In addition, explainable AI XAI tools like Grad-CAM increase transparency by highlighting the lung regions most relevant to the diagnosis. CONCLUSION: The study demonstrates the potential of artificial intelligence, internet of things, and cloud computing to address future pandemic-prone diseases.