International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Automated Non-Invasive Detection of Neonatal Jaundice Using Convolutional Neural Network |
|---|---|
| ABSTRACT | Neonatal jaundice is a common condition in newborns and must be detected early to avoid serious health complications. Conventional detection methods depend on invasive blood sampling, which may cause discomfort and requires laboratory facilities. An automated, non-invasive jaundice detection system that analyzes newborn photos using Convolutional Neural Networks (CNN) is presented in this study.The system captures facial or skin- region images, preprocesses them, and classifies the infant as jaundiced or normal. A web-based interface enables real-time screening using standard imaging devices. The proposed solution aims to provide a fast, accessible, and reliable diagnostic alternative suitable for both clinical and low-resource healthcare settings. A user- friendly online interface created with HTML, CSS, JavaScript, and the Canvas API supports the system and allows for instant image upload and on-screen results without the need for medical knowledge. |
| AUTHOR | BASAVARAJA PATIL G V, ANUSHA G C, BIMBA SIRI J K, CHINMAYI D B, MANGALA G C Department of Computer Science and Engineering, Jain Institute of Technology, Davanagere, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312109 |
| pdf/109_Automated Non-Invasive Detection of Neonatal Jaundice Using Convolutional Neural Network.pdf | |
| KEYWORDS | |
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