Abstract
Neonatal disorder is most common in newborn babies. It is due to the unusual activity of the baby's organs. Neonatal hyperbilirubinemia is familiar among neonates. The origin of hyperbilirubinemia is the existence of yellow coloration on the baby's skin. At a severe stage, it may affect the human brain. Early prediction is essential to overcome the neonate's mortality. It is achieved by computer vision-based image preprocessing and machine learning. In the existing system, feature extraction is based on color features. To enhance the performance, multiple extractions such as structural feature extraction and pixel positioning feature extraction are essential for medical image feature extraction. In the proposed system, a feature fusion method is implemented to upgrade the performance. Developing the novel method of the feature fusion framework enhances the performance of the binary classification and helps for up-gradation. The system architecture consists of baby segmentation, feature fusion, and classification. Baby segmentation is done by the fast and robust fuzzy clustering algorithm. Feature fusion framework includes feature extraction, feature fusion, and feature selection. Feature extraction is performed by the novel method of Structural Color Gray Coherent Feature extraction to extract 35 features. The features are fused into a single feature by a concatenation operation. An improvised method of the Salp Swarm Algorithm is implemented for feature selection, and the classification is done by a random forest. The comparison analysis of various classifiers is carried out, based on their true positive, true negative, false negative, and false positive, respectively. The experimental result shows the random forest classified with a performance of 97% accuracy.
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Nayagi, S.B., Angel, T.S.S. An enhanced method of feature fusion techniques to diagnosis neonatal hyperbilirubinemia. Soft Comput 27, 10961–10974 (2023). https://doi.org/10.1007/s00500-023-08565-3
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DOI: https://doi.org/10.1007/s00500-023-08565-3