Master thesis defense on “Non-Invasive Infant Jaundice Level Estimation from Images of the Skin Using Machine Learning”
mohammedhassan2025-02-27T10:04:53+00:00A Master thesis was discussed in Department of Computer Engineering/College of Engineering at University of Mosul entitled “Non-Invasive Infant Jaundice Level Estimation from Images of the Skin Using Machine Learning” submitted by the student (Banan Khalid Abdulkader Al-Dabbagh) on Wednesday, Feb. 19, 2025. The study investigates a non-invasive, image-based technique for detecting and classifying neonatal jaundice severity using advanced Machine Learning (ML) technique. A new dataset of 344 images of infants with Jaundice and healthy of full-term newborns were created with four sub-datasets for classification. The research employs Deep Transfer Learning (DTL) with pre-trained models (VGG16, ResNet50, EfficientNet) and the K-Nearest [Read More]