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- W4367175958 abstract "In the first few days of life, neonatal jaundice is the situation that causes yellow discoloration on the skin of the baby. The severity level of the disease is determined by using a bilirubin meter. It may lead to poor performance due to a lack of severity level identification. The severity level should be resolved by using some techniques or methodology. Therefore, the proposed system is in the method of developing feature fusion techniques and CNN-based severity level detection to enhance the diagnosis of the disease. In this work, first, data are grouped as jaundiced babies and healthy babies. Consider the babies among 24 and 48 hr after birth. It is performed to remove the small hairs and noises from the baby's skin. Fourth, the Feature Fusion Framework to extract and select highly informative features. It includes feature extraction, feature fusion, and feature selection respectively. Feature extraction is carried out by a novel method of Structural Color Grey Coherent Feature (SCGCF) Extraction, feature fusion by a concatenation operation, and feature selection by the enhanced method of Salp Swarm Optimization. Sixth, the CNN model was trained along with color-card techniques to identify their severity level. The performance evaluation of the model provides class 1 and class 2 sensitivity of 0.99 and 0.98, specificity of 0.98 and 0.98, the accuracy of the 0.98 and 0.98, MCC of 0.97 and 0.96, and f-measure of 0.98 and 0.98 respectively." @default.
- W4367175958 created "2023-04-28" @default.
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- W4367175958 date "2023-08-01" @default.
- W4367175958 modified "2023-09-27" @default.
- W4367175958 title "Diagnosis of neonatal hyperbilirubinemia using CNN model along with color card techniques" @default.
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- W4367175958 doi "https://doi.org/10.1016/j.bspc.2023.104746" @default.
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