Abstract

        Jaundice is a common condition for newborns, and its complications can be severe and cause permanent damage to the patient’s brain if no action is taken at its early stages. Current methods for jaundice detection are invasive, which include collecting blood samples from the patient, which can be painful and stressful and may cause some complications. Alternatively, a non-invasive approach can be used to diagnose jaundice through image-processing and artificial intelligence (AI) techniques, requiring a database of infant images to achieve a high-accuracy diagnosis. This data article provides a collection of newborn images, called NJN, with various birthweight and skin tones, with ages ranging from 2 to 8 days, and an excel sheet file in CSV format for the values of RGB and YCrCb channels and the status for each raw which is freely accessible at (https://sites.google.com/view/neonataljaundice). It also provides Python code for data testing using different AI techniques. Thus, this article offers a unique resource for all AI researchers to train their AI system and develop algorithms to help neonatal intensive care unit (NICU) healthcare specialists monitor neonates and provide fast, real-time, non-invasive, and accurate jaundice diagnosis.

Data Description

    This dataset article provides images of newborns taken in the NICU at Al-Elwiya Maternity Teaching Hospital in Al Rusafa, Baghdad, Iraq. It is a hospital specializing in obstetrics and gynecology; therefore, all infants are considered aseptic. This data com-prises normal and jaundiced infant images from different angles and lighting environments. Thus, collecting as many images as possible helps increase the accuracy. The collected data from about 600 newborns, includes 670 infant images (560 normal and 200 jaundiced) with 1000 ×1000 resolution all in jpg format. The images were taken by an iPhone 11 pro max 12 MP camera. The dataset is composed of three folders: normal neonate images, jaundiced neonate images, and an excel sheet file in CSV (Comma delimited) format that contains the RGB and YCrCb channel values in addition to the status of row values, either “1” for normal, or “2” for jaundiced.      

Dataset Downloading

• Normal infants dataset (1000×1000)

• Jaundice Infants dataset (1000×1000)

• Pythons_code_AI_Testing_Data

Please cite the following publication when you use this data.

Publication

        For further information about the dataset

[1] Abdulrazzak, A.Y.; Mohammed, S.L.; Al-Naji, A. NJN: A Dataset for the Normal and Jaundiced Newborns. BioMedInformatics 2023, 3, 543-552. https://doi.org/10.3390/biomedinformatics3030037 

Acknowledgment

      The authors show their gratitude and appreciation to Middle Technical University, Electrical Engineering Technical College-Baghdad, Iraq, for the support and encouragement for disseminating scientific engineering research and to Al Elwiya Maternity Teaching Hospital for providing the required data to perform this study.