Neonatal Jaundice Severity Detection from Skin Images using Deep Transfer Learning Techniques

Abstract

Neonates in the initial weeks postpartum frequently experience jaundice, a prevalent medical condition characterized by the yellow discoloration of the sclera and integumentary surfaces. This phenomenon transpires as a result of the elevation of bilirubin concentrations within the circulatory system. When bilirubin levels reach critical thresholds, they present a considerable risk for severe complications, including neurological impairment, which represents one of the gravest outcomes that may ensue if the condition is not addressed with due diligence. This study investigates a non-invasive method for assessing jaundice severity in full-term infants from 1 to 29 days, focusing on infants in Mosul city. A dataset of 344 images was collected using an iPhone 12 Pro Max (9MP camera) at Ibn Al-Atheer Hospital, capturing various skin tones and lighting conditions to ensure accurate analysis. Advanced computer vision techniques were used to classify jaundice severity into three and four categories based on skin images. Pre-trained deep transfer learning models, namely VGG16 and ResNet50, were utilized for training, with the fully connected layer removed and a suitable classifier designed for each model. VGG16 achieved 91.71% accuracy for the three-category classification, while ResNet50 reached 95.98%. For the four-category classification, accuracies of 94.92% and 94.66% were achieved, respectively. These high accuracy levels suggest that non-invasive, image-based assessments can reduce the need for repeated blood tests. This research highlights the potential of using smartphone-based methods for jaundice screening in neonatal care, providing a reliable, accessible tool to reduce strain on medical facilities and improve early detection.