Class-Imbalance Aware Learning for Enhanced Noninvasive Neonatal Jaundice Diagnosis – Using Convnext-Tiny with Focal Loss (CNXT-FL) - Krishnaraj, Sathyaseelan; Subramaniam, Sarathambekai; Krishnamoorthy, Umapathi (International Journal of Industrial Engineering: Theory, Applications and Practice, 2026)
Early diagnosis of neonatal jaundice carries significance as it could prevent serious neurological problems. Conventional diagnostics based on assessment of total serum bilirubin (TSB) in blood samples are invasive, painful, time-consuming, and require a laboratory setting. As such facilities are often obscure in resource-limited remote locations, AI-based image analysis was frequently investigated as an alternate solution to optimize clinical workflow. However, the need for an optimized healthcare decision support system remains a research gap. This research introduces a Transformer-inspired ConvNeXt-Tiny architecture with Focal Loss (CNXt-Fl) for rapid, scalable, and accurate diagnosis of neonatal jaundice. The proposed framework includes (i) Albumentations-based augmentation for ensuring model robustness to image acquisition conditions, (ii) ‘focal loss’ for class-weighted learning to address class imbalance, and (iii) a pretrained ConvNeXt-Tiny model to ensure insensitivity to small datasets. Experimental results from CNXt-Fl evaluated on a public neonatal jaundice dataset consisting of 760 neonatal images achieved an accuracy and F1-score of 0.9912 and 0.99, respectively. Further, comparison with conventional CNN, other pre-trained models, and state-of-the-art deep learning models revealed that the proposed model achieves better results comparatively. These results highlight the potential of lightweight transformer-inspired architectures with class-imbalance aware learning for reliable early detection of neonatal jaundice in real-world healthcare environments. Thus, this work holds significance in the optimization of healthcare decision support systems.