Acupoint Detection Based on Deep Convolutional Neural Network
Lingyao Sun, Shiying Sun, Yuanbo Fu, Xiaoguang Zhao
Abstract
As an important component of Traditional Chinese Medicine (TCM), science of acupoint therapy has achieved significant results in clinical practice, but recognizing and positioning acupoints is heavily depends on the skills of practitioners. In recent years, researchers have proposed a few methods of automatic acupoints detection and positioning, but most of the methods are still based on manual designed features. In this paper, we propose an acupoints detection method based on deep convolutional neural network, and an evaluation method is proposed for acupoint detection. What's more, we build an acupoint detection dataset. Experiments are performed and a promising result is achieved.
Topics & Concepts
Convolutional neural networkComputer scienceArtificial intelligenceDeep learningClinical PracticeArtificial neural networkFeature extractionPattern recognition (psychology)Machine learningMedicineFamily medicineTraditional Chinese Medicine StudiesAcupuncture Treatment Research StudiesHuman Pose and Action Recognition