Tongue Image Retrieval Based On Reinforcement Learning

Abstract

In Chinese medicine, the patient's body constitution plays a crucial role in determining the course of treatment because it is so intrinsically linked to the patient's physiological and pathological processes. Traditional Chinese medicine practitioners use tongue diagnosis to determine a person's constitutional type during an examination. An effective solution is needed to overcome the complexity of this setting before the tongue image constitution recognition system can be deployed on a non-invasive mobile device for fast, efficient, and accurate constitution recognition. We will use deep deterministic policy gradients to implement tongue retrieval techniques. We suggested a new method for image retrieval systems based on Deep Deterministic Policy Gradients (DDPG) in an effort to boost the precision of database searches for query images. We present a strategy for enhancing image retrieval accuracy that uses the complexity of individual instances to split the dataset into two subsets for independent classification using Deep reinforcement learning. Experiments on tongue datasets are performed to gauge the efficacy of our suggested approach; in these experiments, deep reinforcement learning techniques are applied to develop a retrieval system for pictures of tongues affected by various disorders. Using our proposed strategy, it may be possible to enhance image retrieval accuracy through enhanced recognition of tongue diseases. Databases containing pictures of tongues affected by a wide range of disorders will be used as examples. The experimental results suggest that the new approach to computing the main colour histogram outperforms the prior one. Though the difference is tiny statistically, the enhanced retrieval impact is clear to the human eye. The tongue is similarly brought to the fore to emphasise the importance of the required verbal statement. Both investigations used tongue images classified into five distinct categories.