http://www.iaeme.com/IJEET/index.asp 248 editor@iaeme.com International Journal of Electrical Engineering and Technology (IJEET) Volume 11, Issue 3, May 2020, pp. 248-264, Article ID: IJEET_11_03_029 Available online at http://www.iaeme.com/IJEET/issues.asp?JType=IJEET&VType=11&IType=3 ISSN Print: 0976-6545 and ISSN Online: 0976-6553 Journal Impact Factor (2020): 10.1935 (Calculated by GISI) www.jifactor.com © IAEME Publication NEONATAL JAUNDICE DETECTION SYSTEM USING CNN ALGORITHM AND IMAGE PROCESSING Ashish Chakraborty School of Electronics and Communication Engineering, Vellore Institute of Technology University, Vellore, India Sushil Goud School of Electronics and communication Engineering Vellore Institute of Technology University, Vellore, India Vandita Shetty School of Electronics and Communication Engineering, Vellore Institute of Technology University, Vellore, India Budhaditya Bhattacharyya School of Electronics and Communication Engineering, Vellore Institute of Technology University, Vellore, India ABSTRACT Neonatal hyperbilirubinemia or jaundice is a common health condition in newborn infants because of changes in erythrocyte metabolism in the first week of life itself. It is a multifactorial disorder with many symptoms. With today’s technological advancements, we have both invasive and non - invasive systems to facilitate early neonatal jaundice detection and subsequent treatment at the early stages itself. In this paper, we shall discuss our proposed non-invasive neonatal jaundice detection system using CNN algorithm. The various detection systems stated in this paper provides the accuracy of the method and feasibility when it comes to the implementation. All methodologies and detection techniques discussed here provide real-world insight and helps is early detection of neonatal jaundice. These include the use of Support Vector Machines i.e. the SVM with image processing technique that helps in reading different bilirubin levels in the baby at the time of disease. The first regressions which was used was generally linear, but SVR algorithm was non-linear. When determining the relationships between linear relationships, generally, Support Vector Regressions were used. The aim of the regression was finding a linear regression function in a high dimensional feature space. Then, input data was mapped to the space with using the potential non-linear function. Color card is another detection method used wherein, based on the skin and eye’s coloration and comparing with the color cards developed,