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The document presents a study on a Neonatal Jaundice Detection System utilizing Convolutional Neural Network (CNN) algorithms to estimate bilirubin levels in newborns non-invasively through smartphone images. The system employs various CNN architectures, including ResNet50, which achieved the highest accuracy of 84.091%, and aims to reduce the need for invasive blood tests. The dataset consists of 145 images categorized into normal and two abnormal bilirubin levels, with the implementation based on a Raspbe

Author

ssinghsuryansh180

Language

EN