Version 1
: Received: 17 March 2023 / Approved: 21 March 2023 / Online: 21 March 2023 (14:01:10 CET)
How to cite:
Abdulrazzak, A.Y.; Al-Naji, A.; Mohammed, S.L. NJN: A Dataset for the Normal and Jaundiced Newborns. Preprints2023, 2023030379. https://doi.org/10.20944/preprints202303.0379.v1
Abdulrazzak, A.Y.; Al-Naji, A.; Mohammed, S.L. NJN: A Dataset for the Normal and Jaundiced Newborns. Preprints 2023, 2023030379. https://doi.org/10.20944/preprints202303.0379.v1
Abdulrazzak, A.Y.; Al-Naji, A.; Mohammed, S.L. NJN: A Dataset for the Normal and Jaundiced Newborns. Preprints2023, 2023030379. https://doi.org/10.20944/preprints202303.0379.v1
APA Style
Abdulrazzak, A.Y., Al-Naji, A., & Mohammed, S.L. (2023). NJN: A Dataset for the Normal and Jaundiced Newborns. Preprints. https://doi.org/10.20944/preprints202303.0379.v1
Chicago/Turabian Style
Abdulrazzak, A.Y., Ali Al-Naji and Saleem Latteef Mohammed. 2023 "NJN: A Dataset for the Normal and Jaundiced Newborns" Preprints. https://doi.org/10.20944/preprints202303.0379.v1
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.
Keywords
Jaundice; Hyperbilirubinemia; skin color analysis; NICU; artificial intelligence (AI) techniques
Subject
Computer Science and Mathematics, Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
