Computer-Aid System for Automated Jaundice Detection

Abstract

At the beginning of their lives, newborns may have a widespread condition known as Jaundice or Hyperbilirubinemia. High levels of bilirubin in the blood are the primary cause of jaundice. Severe cases of jaundice may cause acute bilirubin encephalopathy due to the toxicity of bilirubin to the cells of the brain, which may lead to kernicterus. Kernicterus causes several symptoms, including a permanent upward look, loss of hearing, and repetitive and uncontrolled movements. Therefore, diagnosing this condition at the appropriate time helps to prevent chronic effects. In this study, jaundice or hyperbilirubinemia is diagnosed using a computer vision system based on a random forest algorithm. The system comprises a digital HD camera, a computer device with a Matlab application installed to analyze and detect the skin color changes of the infant, and an Arduino Uno microcontroller to control an LED ultraviolet light. A set of neonate images were collected to train the random forest algorithm, including 374 for normal and 137 for jaundiced infants. |The experimental results using the random forest algorithm for classification reached an accuracy of 98.4375%. The results of this study are promising and open doors for new monitoring applications in various medical diseases detection with a high degree of accuracy.