Risk Zone Estimation of Newborn Jaundice Based on Skin Color Image Analysis

Juliastuti, Endang; Nadhira, Vebi; Satwika, Yokanan Wigar; Aziz, Naufal Aulia; Zahra, Naila

doi:10.1109/ica.2019.8916752

Cited by 12 publications

(9 citation statements)

References 6 publications

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“…It looked like the researchers had a high correlation. Juliastuti et al [42] suggested a risk-zone estimation framework. The framework employs an advanced camera to collect photographs infants, a middle filter to remove grain from the images, and color rectification to lower color saturation by using a color card containing the three primary colors of RGB.…”

Section: Methodologies Of Neonatal Jaundice Detectionmentioning

confidence: 99%

A Non-invasive Methods for Neonatal Jaundice Detection and Monitoring to Assess Bilirubin Level: A Review

Karim¹,

Zaman²,

Yong³

2023

AETiC

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Neonatal jaundice is a frequent cause of substantial illness and mortality in newborns. The newborn infant’s skin, eyes, and other tissues turn yellow because bilirubin contains a pigment or coloring. Jaundice that manifests in the first few days is highly dangerous and typically needs to be treated right away. It is typically “physiologic” when jaundice emerges on the second or third day. Hyperbilirubinemia refers to an abnormally high bilirubin level in the blood. During the decomposition of red blood cells, bilirubin is formed. Bilirubin can build up in the blood, bodily fluids, and other tissues of newborn babies because they are not naturally able to expel it. Kernicterus or irreversible brain damage can result from untreated jaundice if the abnormally high levels of bilirubin are not controlled. In cases of neonatal jaundice, there is currently a variety of estimating methods for measuring bilirubin levels. The goal of this research is to provide a thorough evaluation of various non-invasive frameworks for the identification of newborn jaundice. For this review article, a critical analysis has done by using 51 articles from 2009 to 2022 where all articles are based on the detection of neonatal jaundice. This literary work on non-invasive methods and neonatal jaundice results appear to be an understanding of the avant-garde procedures created and used in this domain. The review also compares and contrasts different non-invasive strategies for predicting an infant’s state of serum bilirubin based on different data such as social media data, and clinical data. At last, the open issues and future challenges of using a non-invasive method to better understand as well as diagnose the neonatal jaundice state of any individual were discussed. From the literature study, usually apparent that the utilization of non-invasive methods in neonatal jaundice has yielded noteworthy fulfillment within the regions of diagnosis, support, research, and clinical governance.

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Section: Methodologies Of Neonatal Jaundice Detectionmentioning

confidence: 99%

A Non-invasive Methods for Neonatal Jaundice Detection and Monitoring to Assess Bilirubin Level: A Review

Karim¹,

Zaman²,

Yong³

2023

AETiC

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“…Endang Juliastuti et al [5] proposed a methodology to deal with hyperbilirubinemia which causes yellow discoloration on babies' eyes and skin. It is a serious case in the severe stage.…”

Section: Skin Detectionmentioning

confidence: 99%

Detection and Classification of Neonatal Jaundice Using Color Card Techniques – A Study

2022

Int. J. Onl. Eng.

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--- In a newborn baby, the particular complication is jaundice. Jaundice is due to many reasons. An increase in the bilirubin level denotes the presence of jaundice. Neonatal hyperbilirubinemia is most common in the first postnatal week. Early prediction and classification of jaundice are essential to reduce the mortality rate. Diagnosis of the disease using computer vision techniques helps to achieve accuracy in prediction and classification. Here I studied and extracted the information from medical data mining and achieved the goal. It involves examining, analyzing, extracting, selecting, and classifying the features. Finally, it provides treatment suggestions It enables the physician to perform early prediction and provide appropriate jaundice treatment. Computer vision and machine learning techniques make the process easy and simple. The optimized method of classification increases accuracy.

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“…The preliminary takes a look at protecting an odd node solely with sensors because of ambient conditions. In the subsequent phase, the sensors are because growth monitoring or essential symptoms pleasure stand chronic, described in quantity two proposed in [ 17 ]. Predictive modelling can play a vital role in analyzing large sets of population health information to identify the risks of a disease, allowing clinicians to avoid unnecessary therapies that may shorten a patient's life expectancy or have no effect at all.…”

Section: Related Workmentioning

confidence: 99%

Optimized Tree Strategy with Principal Component Analysis Using Feature Selection-Based Classification for Newborn Infant’s Jaundice Symptoms

Samanta

Karthikeyan²,

Karuppiah

et al. 2021

Journal of Healthcare Engineering

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One of the most important and difficult research fields is newborn jaundice grading. The mitotic count is an important component in determining the severity of newborn jaundice. The use of principal component analysis (PCA) feature selection and an optimal tree strategy classifier to produce automatic mitotic detection in histopathology images and grading is given. This study makes use of real-time and benchmark datasets, as well as specific approaches for detecting jaundice in newborn newborns. According to research, the quality of the feature may have a negative impact on categorization performance. Additionally, compressing the classification method for exclusive main properties can result in a classification performance bottleneck. As a result, identifying appropriate characteristics for training the classifier is required. By combining a feature selection method with a classification model, this is possible. The major outcomes of this study revealed that image processing techniques are critical for predicting neonatal hyperbilirubinemia. Image processing is a method of translating analogue images to digital formats and manipulating them. The primary goal of medical image processing is to collect information useful for disease detection, diagnosis, monitoring, and therapy. Image datasets can be used to validate the performance of newborn jaundice detection. When compared to conventional approaches, it offers results that are accurate, quick, and time efficient. Accuracy, sensitivity, and specificity, which are common performance indicators, were also predictive.

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Risk Zone Estimation of Newborn Jaundice Based on Skin Color Image Analysis

Cited by 12 publications

References 6 publications

A Non-invasive Methods for Neonatal Jaundice Detection and Monitoring to Assess Bilirubin Level: A Review

A Non-invasive Methods for Neonatal Jaundice Detection and Monitoring to Assess Bilirubin Level: A Review

Detection and Classification of Neonatal Jaundice Using Color Card Techniques – A Study

Optimized Tree Strategy with Principal Component Analysis Using Feature Selection-Based Classification for Newborn Infant’s Jaundice Symptoms

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