LAUSR

At present, the judgment of the burn depth is mainly based on the experience of doctors, so the accuracy of judgment is low, which will affect the follow-up treatment and nursing. In order to improve the diagnostic effect of clinical burns, based on incremental reinforcement learning algorithms, this paper constructs a classification model of clinical burn thermal images based on machine learning algorithms. Moreover, this paper proposes an adaptive network algorithm and uses the structure of the network to implement a reinforcement learning algorithm. In this algorithm, in order to reduce the computational complexity under the premise of ensuring sample utilization, the parameters are updated in the form of increments. In addition, this paper uses the value function approximator to linearly approximate the value function and TD error. Finally, this paper constructs the basic structure of the model based on the functional requirements and constructs experiments to verify the performance of the model. The research results show that the algorithm has good convergence and the image classification effect is very obvious, so it has certain practical significance.