R-JaunLab: Automatic Multi-Class Recognition of Jaundice on Photos of Subjects with Region Annotation Networks

• Published in: Journal of digital imaging Vol. 34; no. 2; pp. 337 - 350
• Main Authors: Wang, Zheng, Xiao, Ying, Weng, Futian, Li, Xiaojun, Zhu, Danhua, Lu, Fanggen, Liu, Xiaowei, Hou, Muzhou, Meng, Yu
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.04.2021; Springer Nature B.V
• Subjects: Algorithms; Annotations; Artificial intelligence; Artificial neural networks; Bilirubin; Clinical medicine; Comparative studies; Digital imaging; Gallbladder; Gastroenterology; Globalization; Hepatitis; Hepatology; Hospitals; Humans; Imaging; Jaundice; Liver cancer; Localization; Medicine; Medicine & Public Health; Neural networks; Neural Networks, Computer; Original Paper; Pancreas; Pancreatic cancer; Physicians; Radiology; Recognition; Smartphones; Convolutional Neural Network; ); Region Annotation Network; Occult Jaundice; Total Serum Bilirubin; Region Annotation Network (RAN); Total Serum Bilirubin (TBil); Convolutional Neural Network (CNN)

Jaundice occurs as a symptom of various diseases, such as hepatitis, the liver cancer, gallbladder or pancreas. Therefore, clinical measurement with special equipment is a common method that is used to identify the total serum bilirubin level in patients. Fully automated multi-class recognition of j...