A Mobile Computer-Aided Diagnosis of Neonatal Hyperbilirubinemia using Digital Image Processing and Machine Learning Techniques

Abstract

Neonatal Hyperbilirubinemia, or jaundice, is a harmful disease found in newborns, a symptom of which is the yellowish discoloration of the skin. Visual examination is most frequently used for screening of Hyperbilirubinemia in neonates, however, blood specimen collection is the gold standard to identify the disease and its severity. We propose a Mobile Computer-Aided Diagnosis (mCADx) tool to identify the Neonatal Hyperbilirubinemia symptom using advanced digital image processing and data mining techniques. The mCADx was developed in a cross-platform environment. The mCADx works with smart devices run on either iOS or Android operating systems. With ethical committee approval, we collected and studied image data of 178 infant subjects with different jaundice severity levels. The severity of the disease was examined from blood test results, which were annotated by medical specialists. Data mining techniques included Decision Trees, k Nearest Neighbor, and the Conventional Neural Network was investigated in the dataset. An in-depth comparison between techniques was performed and discussed. The classification results in CNN gained the highest accuracy at 0.8099, 0.9251, 0.8086. This novel work can assist in identifying Neonatal Hyperbilirubinemia in newborns after discharging from the hospital. Reoccurring Neonatal Hyperbilirubinemia can be found with minimum awareness of parents. Limitations and future works were discussed in this work.