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Diagnosis of Neonatal Hyperbilirubinemia Using CNN Model Along with Color Card Techniques

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Abstract

In the first few days of life, neonatal jaundice is the situation that causes yellow discoloration on the baby’s skin. The yellowish pigmentation is the gesture of an increase in bilirubin levels. It is common in 2/3 of all active infants. It is a condition caused by the problems of poor breastfeeding, the lifespan of red blood cells, or the hydration level. Every year, about 1.1 million infants are affected by hyperbilirubinemia. The approach and knowledge of the purpose of neonatal jaundice are restricted. Exploring the purpose of neonatal jaundice has superior significance in reducing jaundice-related infant mortality and morbidity. According to pudmed.com, case-control analysis is carried out and produces a medical chart of 272 infants for public healthcare units. Proper diagnosis of the disease is required to reduce the mortality rate. Computer vision techniques are essential to diagnose the disease. It can be done with the appropriate machine learning techniques. The existing system uses machine learning methodology to analyze the presence of disease. 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. Fifth, AdaBoost-Random Forest is carried out to identify the presence of neonatal hyperbilirubinemia. Sixth, the CNN model was trained along with color-card techniques to identify their severity level. Finally, the time series property is included to perform continuous monitoring of neonatal hyperbilirubinemia. The performance evaluation of the model provides class 1 and class 2 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. The maximum epochs are 100 for training the model. The best validation is obtained at the 22nd epoch. The training performance is 0.24991 at epoch 2.

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  1. Department of Computational Intelligence, SRM IST, Kattankulathur, 603203, Tamil Nadu, India

    S. Bharani Nayagi & T. S. Shiny Angel

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  1. S. Bharani Nayagi
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  2. T. S. Shiny Angel
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Nayagi, S.B., Angel, T.S.S. Diagnosis of Neonatal Hyperbilirubinemia Using CNN Model Along with Color Card Techniques. J. Electr. Eng. Technol. 18, 3861–3879 (2023). https://doi.org/10.1007/s42835-023-01460-9

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