Authors : Mohamed E. Mongy; Ahmed Taha; Mai K. Galab

Volume/Issue : Volume 11 - 2026, Issue 10 - October

Google Scholar : https://tinyurl.com/2ryb8ep6

DOI : https://doi.org/10.38124/ijisrt/26Oct1211

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Neonatal jaundice is a common clinical condition during the first week of life and requires early detection to reduce the risk of severe complications associated with elevated bilirubin levels. Conventional diagnostic approaches, including invasive blood sampling and device-based screening, may be costly, time-consuming, or unsuitable for continuous monitoring in resource-constrained settings. This paper presents an automated, non-invasive, and calibration-free framework for neonatal jaundice detection from smartphone images. The proposed framework first detects neonatal facial regions using YOLOv8 and then applies the Segment Anything Model (SAM) to obtain accurate facial skin segmentation. The segmented regions are enhanced using Contrast Limited Adaptive Histogram Equalization (CLAHE), followed by image augmentation to improve model robustness. Color-based features are extracted from RGB, YCbCr, HSV, and LAB color spaces and evaluated using six machine learning classifiers: Multi-Layer Perceptron (MLP), Decision Tree (DT), KNearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost).

Keywords : Neonatal Jaundice, Bilirubin, Hyperbilirubinemia, Total Serum Bilirubin, Transcutaneous Bilirubin.

References :

1. L. M. Gartner, “Neonatal jaundice,” Pediatrics in Review, vol. 15, no. 11, pp. 422–432, 1994.
2. J. Rennie, S. Burman-Roy, and M. S. Murphy, “Neonatal jaundice: Summary of NICE guidance,” BMJ, vol. 340, 2010.
3. P. Padidar et al., “Detection of neonatal jaundice by using an Android OS-based smartphone application,” Iranian Journal of Pediatrics, vol. 29, no. 2, 2019.
4. F. A. Dzulkifli, M. Y. Mashor, and K. Khalid, “Methods for determining bilirubin level in neonatal jaundice screening and monitoring: A literature review,” J. Eng. Res. Educ., vol. 10, pp. 1–10, 2018.
5. P. Puppalwar, K. Goswami, and A. Dhok, “Review on evolution of methods of bilirubin estimation,” IOSR Journal of Dental and Medical Sciences, vol. 1, no. 3, pp. 17–28, 2012.
6. W. Chee et al., “Jaundice in infants and children: Causes, diagnosis and management,” Hong Kong Medical Journal, 2018.
7. S. Ullah, K. Rahman, and M. Hedayati, “Hyperbilirubinemia in neonates: Types, causes, clinical examinations, preventive measures and treatments,” Iranian Journal of Public Health, vol. 45, no. 5, pp. 558–568, 2016.
8. K. Mreihil et al., “Phototherapy is commonly used for neonatal jaundice but greater control is needed to avoid toxicity in the most vulnerable infants,” Acta Paediatrica, vol. 107, no. 4, pp. 611–619, 2018.
9. B. Katti, “Analytical review on technologies designed for detection and treatment of neonatal jaundice,” Int. J. Sci. Res., vol. 8, p. 2518, 2021.
10. A. R. Deepthi, C. Singh, and J. B. Jeeva, “A device to detect hyperbilirubin through non-invasive technique,” in Proc. i-PACT, 2019.
11. R. Singla and S. Singh, “A framework for detection of jaundice in newborn babies using homomorphic filtering,” in Proc. ICICT, 2016.
12. R. Keren et al., “Visual assessment of jaundice in term and late preterm infants,” Arch. Dis. Child. Fetal Neonatal Ed., vol. 94, no. 5, pp. F317–F322, 2009.
13. A. Riskin et al., “Is visual assessment of jaundice reliable as a screening tool?” J. Pediatrics, vol. 152, no. 6, pp. 782–787, 2008.
14. C. Aranda, D. Torrubia, and L. Álvarez, “Correlation of serum, capillary and transcutaneous bilirubin,” Biomed. J. Sci. Tech. Res., vol. 1, no. 3, pp. 722–726, 2017.
15. K. Srividya et al., “Review on jaundice detection in neonates using image processing,” in Proc. IC3IoT, 2022.
16. A. Althnian, N. Almanea, and N. Aloboud, “Neonatal jaundice diagnosis using a smartphone camera,” Sensors, vol. 21, no. 21, 2021.
17. W. Hashim et al., “Neonatal jaundice detection using a computer vision system,” Designs, vol. 5, no. 4, p. 63, 2021.
18. S. Leartveravat, “Transcutaneous bilirubin measurement by digital camera,” Medical Journal of Srisaket Surin Buriram Hospitals, vol. 24, no. 1, pp. 105–118, 2009.
19. M. N. Mansor et al., “Jaundice monitoring using color detection,” Procedia Engineering, vol. 29, pp. 1631–1635, 2012.
20. M. Mansor et al., “New newborn jaundice monitoring scheme,” Neurocomputing, vol. 120, pp. 258–261, 2013.
21. T. S. Leung et al., “Screening neonatal jaundice based on sclera color,” Biomedical Optics Express, vol. 6, no. 11, pp. 4529–4538, 2015.
22. M. Aydın et al., “Neonatal jaundice detection system,” Journal of Medical Systems, vol. 40, pp. 1–11, 2016.
23. A. Mariakakis et al., “BiliScreen: Smartphone-based scleral jaundice monitoring,” Proc. ACM IMWUT, vol. 1, no. 2, Art. no. 20, 2017.
24. F. Outlaw et al., “Screening for neonatal jaundice with a smartphone,” in Proc. Digital Health Conf., 2017.
25. S. B. Munkholm et al., “Smartphone camera for transcutaneous bilirubin,” PLOS One, vol. 13, no. 6, p. e0197938, 2018.
26. E. Juliastuti et al., “Risk zone estimation of newborn jaundice,” in Proc. ICA, IEEE, 2019.
27. F. Hardalac et al., “Classification of neonatal jaundice in mobile applications,” Turkish J. Electr. Eng. Comput. Sci., vol. 29, no. 4, pp. 2116–2126, 2021.
28. W. Hashim et al., “Computer vision for jaundice detection,” in IOP Conf. Series: Mater. Sci. Eng., 2021.
29. S. Dissaneevate et al., “Mobile computer-aided diagnosis of neonatal hyperbilirubinemia,” Int. J. Innov. Res. Sci. Stud., vol. 5, no. 1, pp. 10–17, 2022.
30. D. Kalbande et al., “Deep learning approach for early diagnosis of jaundice,” in Proc. ICICC, vol. 3, Springer, 2022.
31. B. Sreedha, P. R. Nair, and R. Maity, “Non-invasive early diagnosis of jaundice,” Procedia Computer Science, vol. 218, pp. 1321–1334, 2023.
32. J. Prajapati et al., “jScan: Smartphone-assisted bilirubin quantification,” IEEE Sensors Journal, 2023.
33. J.-H. Kim, N. Kim, and C. S. Won, “High-speed drone detection based on YOLOv8,” in Proc. ICASSP, IEEE, 2023.
34. D. Qi et al., “YOLO5Face: Why reinventing a face detector,” in ECCV, Springer, 2022.
35. W. Yang and Z. Jiachun, “Real-time face detection based on YOLO,” in Proc. ICKII, IEEE, 2018.
36. A. Kirillov et al., “Segment anything,” in Proc. IEEE/CVF ICCV, 2023.
37. J. Redmon et al., “You only look once: Unified, real-time object detection,” in Proc. CVPR, 2016.
38. A. M. Reza, “Realization of CLAHE for real-time image enhancement,” J. VLSI Signal Process., vol. 38, pp. 35–44, 2004.