Open AccessAutomatic evaluation of traumatic brain injury based on terahertz imaging with machine learning
Author(s) -
Jia Shi,
Yuye Wang,
Tunan Chen,
Degang Xu,
Hengli Zhao,
Linyu Chen,
Chao Yan,
Longhuang Tang,
Yixin He,
Hua Feng,
Jianquan Yao
Publication year - 2018
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.26.006371
Subject(s) - artificial intelligence , computer science , traumatic brain injury , pattern recognition (psychology) , feature selection , feature extraction , histogram , bottleneck , feature (linguistics) , receiver operating characteristic , machine learning , medicine , linguistics , philosophy , psychiatry , image (mathematics) , embedded system
The imaging diagnosis and prognostication of different degrees of traumatic brain injury (TBI) is very important for early care and clinical treatment. Especially, the exact recognition of mild TBI is the bottleneck for current label-free imaging technologies in neurosurgery. Here, we report an automatic evaluation method for TBI recognition with terahertz (THz) continuous-wave (CW) transmission imaging based on machine learning (ML). We propose a new feature extraction method for biological THz images combined with the transmittance distribution features in spatial domain and statistical distribution features in normalized gray histogram. Based on the extracted feature database, ML algorithms are performed for the classification of different degrees of TBI by feature selection and parameter optimization. The highest classification accuracy is up to 87.5%. The area under the curve (AUC) scores of the receiver operating characteristics (ROC) curve are all higher than 0.9, which shows this evaluation method has a good generalization ability. Furthermore, the excellent performance of the proposed system in the recognition of mild TBI is analyzed by different methodological parameters and diagnostic criteria. The system can be extensible to various diseases and will be a powerful tool in automatic biomedical diagnostics.