Image recognition technology is one of the important research topics in the field of computer vision, which has been widely used in face recognition, aircraft recognition and unmanned driving. As an important research field of computer vision, image target recognition mainly uses the computer to extract the feature information of the target from the acquired image, transforms the content of the image into the feature expression that can be processed by the computer, and classifies the target objects in the image through the appropriate classification algorithm. Compared with traditional image recognition methods, deep learning can learn more complex knowledge. The excellent deep network model can extract the most useful information from the training data, play a good role in generalization, and has a stronger ability to predict the unknown data. For image classification and image recognition, convolutional neural network layer is used to extract image features. The complex network can make large-scale image classification possible. Combined with the specially designed network structure, the target in the image can be located. In this paper, a medical burn image recognition system is constructed by using convolutional neural network technology and deep learning. The proposed model has better robustness compared with existing algorithms.

图像识别技术是计算机视觉领域的重要研究课题之一，已广泛应用于人脸识别，飞机识别和无人驾驶中。图像目标识别是计算机视觉的重要研究领域，主要利用计算机从获取的图像中提取目标的特征信息，将图像的内容转换为计算机可以处理的特征表达，并对图像进行分类。通过适当的分类算法对图像中的目标对象进行分类。与传统的图像识别方法相比，深度学习可以学习更多复杂的知识。优秀的深度网络模型可以从训练数据中提取最有用的信息，在泛化中起很好的作用，并且具有更强的未知数据预测能力。对于图像分类和图像识别，使用卷积神经网络层提取图像特征。复杂的网络可以使大规模图像分类成为可能。结合专门设计的网络结构，可以定位图像中的目标。本文利用卷积神经网络技术和深度学习技术构建了医学烧伤图像识别系统。与现有算法相比，该模型具有更好的鲁棒性。利用卷积神经网络技术和深度学习技术构建了医学烧伤图像识别系统。与现有算法相比，该模型具有更好的鲁棒性。利用卷积神经网络技术和深度学习技术构建了医学烧伤图像识别系统。与现有算法相比，该模型具有更好的鲁棒性。