Manuscript received July 16, 2025; revised July 24, 2025; accepted August 28, 2025; published December 19, 2025.

Abstract—Neonatal jaundice is a common and potentially serious condition that, if left undiagnosed or untreated, can lead to severe neurological complications in newborns. Existing diagnostic methods are often invasive and face limitations in accuracy, accessibility, and data availability, especially in resource-constrained environments. This study introduces NeoViM, an adapted MambaVision-based framework for neonatal jaundice classification. The framework adopts a two-stage approach: In the first stage, the adapted MambaVision is used as a deep feature extractor. Self-Relative Clustering (SRC) is then applied to learn discriminative features by organizing images into clinically meaningful clusters, enabling effective unsupervised learning from limited data. To further enhance feature quality, a self-supervised learning strategy based on Linear Kernel Centered Alignment (LCKA) loss is employed to refine the extracted representations. In the second stage, the pre-trained model is fine-tuned on a small labeled dataset, allowing the system to adapt the learned features for accurate jaundice classification. The methodology was evaluated using the publicly available NJN dataset and achieved a classification accuracy of 93.42% and an F1 score of 93.37%, outperforming previous methods applied to the same dataset. This two-step framework ensures high diagnostic performance while maintaining scalability and accessibility across diverse clinical settings.

Keywords—neonatal jaundice diagnosis, non-invasive, Mamba Vision, deep learning, self-supervised learning

Cite: Fati Oiza Salami, Youssef Mourchid, Muhammad Muzammel, and Alice Othmani, "Vision Mamba-Based Dual-phase Self-supervised Framework for Neonatal Jaundice Diagnosis," Journal of Image and Graphics, Vol. 13, No. 6, pp. 702-708, 2025.

Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC-BY-4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.