The mobile healthcare industry like telemedicine and self-diagnosis are growing with development of IT technology. Jaundice is yellowish pigmentation of the skin and eyes caused by high total bilirubin (T-bilirubin) level in blood due to diseases in the liver, biliary tract and pancreas or a remarkable degradation of their function. Due to measure jaundice which is an important indicator of diseases in these organs, patient must periodically come to the hospital and measure the blood T-bilirubin level through blood collection to trace the changes. In this paper, we proposed image-based jaundice self-diagnostic system using deep learning for those inconvenience and high accuracy jaundice diagnosis. Proposed system consist of a pre-processing unit and a deep learning unit. The pre-processing unit applies color constancy algorithm using patch in image of patient from mobile device, extracts features from segmented sclera area in image. The deep learning unit has 2 stage of deep neural network for high accuracy of estimated T-bilirubin. In first stage, classification network determine whether severe jaundice or not. In next stage, regression network estimate T-bilirubin level. The proposed method was trained and tested using 979 cases of 86 patient from Ajou university hospital with IRB. The test accuracy is 0.93 and AUC is 0.96 in classification network, MAE is 0.0778 in regression network.
