Traditional Chinese medicine-based pattern differentiation system of deficiency and excess using traditional Chinese...

With the development of deep learning technology, the autonomous analytical performance of Traditional Chinese Medicine (TCM) inspections has greatly advanced in recent decades, particularly in the areas of tongue and face diagnosis. To improve the effectiveness of diagnosis and treatment in clinical practice, TCM doctors typically differentiate between TCM-based deficiency and excess based on patterns. Therefore, an accurate TCM-based deficiency and excess pattern differentiation system is required to support TCM doctors in their work, including online diagnosis and treatment, applications on major health platforms, and other situations. This study aimed to develop a TCM-based inspection characteristic extraction model based on convolutional neural networks to extract significant characteristics from the face, lips, tongue, and other areas. Based on TCM theory and the clinical expertise of doctors, mapping modules were created for TCM-based deficiency and excess. These two modules were combined to provide a thorough TCM-based deficiency and excess pattern differentiation system. The experimental results showed that the average accuracy for inspection characteristics, such as tongue body color, coating color, and coating thickness, as well as lip color reached 90% in tests on the gathered facial dataset. In addition, the average accuracy attained 81.67%.for the trained TCM-based deficiency and excess pattern differentiation system.