Computer-Aid System for Automated Jaundice Detection
At the beginning of their lives, newborns may have a widespread condition known as Jaundice or Hyperbilirubinemia. High levels of bilirubin in the blood are the primary cause of jaundice. Severe cases of jaundice may cause acute bilirubin encephalopathy due to the toxicity of bilirubin to the cells...
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middle technical university
2023-03-01
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| Series: | Journal of Techniques |
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| Online Access: | https://journal.mtu.edu.iq/index.php/MTU/article/view/1128 |
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| author | Ahmad Yaseen Abdulrazzak Saleem Latif Mohammed Ali Al-Naji Javaan Chahl |
| author_facet | Ahmad Yaseen Abdulrazzak Saleem Latif Mohammed Ali Al-Naji Javaan Chahl |
| author_sort | Ahmad Yaseen Abdulrazzak |
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At the beginning of their lives, newborns may have a widespread condition known as Jaundice or Hyperbilirubinemia. High levels of bilirubin in the blood are the primary cause of jaundice. Severe cases of jaundice may cause acute bilirubin encephalopathy due to the toxicity of bilirubin to the cells of the brain, which may lead to kernicterus. Kernicterus causes several symptoms, including a permanent upward look, loss of hearing, and repetitive and uncontrolled movements. Therefore, diagnosing this condition at the appropriate time helps to prevent chronic effects. In this study, jaundice or hyperbilirubinemia is diagnosed using a computer vision system based on a random forest algorithm. The system comprises a digital HD camera, a computer device with a Matlab application installed to analyze and detect the skin color changes of the infant, and an Arduino Uno microcontroller to control an LED ultraviolet light. A set of neonate images were collected to train the random forest algorithm, including 374 for normal and 137 for jaundiced infants. |The experimental results using the random forest algorithm for classification reached an accuracy of 98.4375%. The results of this study are promising and open doors for new monitoring applications in various medical diseases detection with a high degree of accuracy.
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| format | Article |
| id | doaj-art-9eec3e2f40ee4bc78f176e1ea35279ec |
| institution | Kabale University |
| issn | 1818-653X 2708-8383 |
| language | English |
| publishDate | 2023-03-01 |
| publisher | middle technical university |
| record_format | Article |
| series | Journal of Techniques |
| spelling | doaj-art-9eec3e2f40ee4bc78f176e1ea35279ec2025-01-19T11:01:56Zengmiddle technical universityJournal of Techniques1818-653X2708-83832023-03-015110.51173/jt.v5i1.1128Computer-Aid System for Automated Jaundice DetectionAhmad Yaseen Abdulrazzak0Saleem Latif Mohammed1Ali Al-Naji 2Javaan Chahl 3Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.School of Engineering, University of South Australia, Mawson Lakes, SA 5095, AustraliaSchool of Engineering, University of South Australia, Mawson Lakes, SA 5095, Australia At the beginning of their lives, newborns may have a widespread condition known as Jaundice or Hyperbilirubinemia. High levels of bilirubin in the blood are the primary cause of jaundice. Severe cases of jaundice may cause acute bilirubin encephalopathy due to the toxicity of bilirubin to the cells of the brain, which may lead to kernicterus. Kernicterus causes several symptoms, including a permanent upward look, loss of hearing, and repetitive and uncontrolled movements. Therefore, diagnosing this condition at the appropriate time helps to prevent chronic effects. In this study, jaundice or hyperbilirubinemia is diagnosed using a computer vision system based on a random forest algorithm. The system comprises a digital HD camera, a computer device with a Matlab application installed to analyze and detect the skin color changes of the infant, and an Arduino Uno microcontroller to control an LED ultraviolet light. A set of neonate images were collected to train the random forest algorithm, including 374 for normal and 137 for jaundiced infants. |The experimental results using the random forest algorithm for classification reached an accuracy of 98.4375%. The results of this study are promising and open doors for new monitoring applications in various medical diseases detection with a high degree of accuracy. https://journal.mtu.edu.iq/index.php/MTU/article/view/1128JaundiceHyperbilirubinemiaPhototherapySkin Color AnalysisRandom Forest Algorithm |
| spellingShingle | Ahmad Yaseen Abdulrazzak Saleem Latif Mohammed Ali Al-Naji Javaan Chahl Computer-Aid System for Automated Jaundice Detection Journal of Techniques Jaundice Hyperbilirubinemia Phototherapy Skin Color Analysis Random Forest Algorithm |
| title | Computer-Aid System for Automated Jaundice Detection |
| title_full | Computer-Aid System for Automated Jaundice Detection |
| title_fullStr | Computer-Aid System for Automated Jaundice Detection |
| title_full_unstemmed | Computer-Aid System for Automated Jaundice Detection |
| title_short | Computer-Aid System for Automated Jaundice Detection |
| title_sort | computer aid system for automated jaundice detection |
| topic | Jaundice Hyperbilirubinemia Phototherapy Skin Color Analysis Random Forest Algorithm |
| url | https://journal.mtu.edu.iq/index.php/MTU/article/view/1128 |
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