A HYBRID DIAGNOSTIC FRAMEWORK FOR EARLY DETECTION OF JAUNDICE USING BIOMEDICAL IMAGING AND NANO-ENABLED BIOSENSORS

This can be challenging to diagnose jaundice, a clinical manifestation of elevated bilirubin brought by liver failure, at its early stage when the symptoms may not be felt. In order to enable initial, non-invasive jaundice detection, this research introduces a hybrid diagnostic system that combines biomedical image analysis with nano-activated biosensor. A xed nervous network (CNN) -Nabed computer Vision module evaluates images to determine facial and scalaral dyscolament, while functional gold nanops (Aunps) are detected in saliva and serum samples. Both test ndings discovered a mutual accuracy of 94.1%, with images of 1200 patients and 50 biosensor samples. It is often found in the eye assessment. The system allows for real-time point-of-care diagnosis and has the potential to be integrated into mobile health platforms, making it accessible for use in rural and resource-poor environments.