Integrating vision transformer-based deep learning model with kernel extreme learning machine for non-invasive diagnosis of neonatal jaundice using biomedical images.
Journal:
Scientific reports
Published Date:
Jul 15, 2025
Abstract
Birth complications, particularly jaundice, are one of the leading causes of adolescent death and disease all over the globe. The main severity of these illnesses may diminish if scholars study more about their sources and progress toward effective treatment. Assured developments were prepared, but they are inadequate. Newborns repeatedly have jaundice as their primary medical concern. A raised level of bilirubin is a symbol of jaundice. Generally, in newborns, hyperbilirubinemia peaks in the initial post-delivery week. The inability to perceive issues early is sufficient for quick treatment, and the resemblance of indications might lead to misdiagnosis. Therefore, appropriate technologies are instantly required. Nowadays, researchers have begun to implement an image-processing model for analyzing jaundice. Paediatricians can detect and classify neonatal jaundice with machine learning (ML) and deep learning (DL) techniques. This study proposes an Early Diagnosis of Neonatal Jaundice Image Classification Using Kernel Extreme Learning Machine (EDNJIC-KELM) approach in the Healthcare Sector. The main intention of the EDNJIC-KELM approach is to build an effective system for diagnosing neonatal jaundice based on advanced methods. Initially, the image pre-processing stage applies the Wiener filtering (WF) method to improve the quality of an image and make it more suitable for analysis by removing the noise. In addition, the vision transformer (ViT) method is employed for the feature extraction process. Furthermore, the EDNJIC-KELM method employs the kernel extreme learning machine (KELM) method for the jaundice image classification. Finally, the enhanced coati optimization algorithm (ECOA) method is implemented for the hyperparameter tuning of the KELM method, which results in a higher classification process. The experimental analysis of the EDNJIC-KELM technique is examined using the Jaundice Image data. The performance validation of the EDNJIC-KELM technique portrayed a superior accuracy value of 96.97% over existing models.
