With the gradual improvement of people’s living standards, the production and drinking of all kinds of food is increasing. People’s disease rate has increased compared with before, which leads to the increasing number of medical image processing. Traditional technology cannot meet most of the needs of medicine. At present, convolutional neural network (CNN) algorithm using chaotic recursive diagonal model has great advantages in medical image processing and has become an indispensable part of most hospitals. This paper briefly introduces the use of medical science and technology in recent years. The hybrid algorithm of CNN in chaotic recursive diagonal model is mainly used for technical research, and the application of this technology in medical image processing is analysed. The CNN algorithm is optimized by using chaotic recursive diagonal model. The results show that the chaotic recursive diagonal model can improve the structure of traditional neural network and improve the efficiency and accuracy of the original CNN algorithm. Then, the application research and comparison of medical image processing are performed according to CNN algorithm and optimized CNN algorithm. The experimental results show that the CNN algorithm optimized by chaotic recursive diagonal model can help medical image automatic processing and patient condition analysis.

中文翻译：

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基于混沌递归对角模型的CNN算法在医学图像处理中的应用

随着人们生活水平的逐步提高，各类食品的生产和饮用日益增多。人们的患病率较以前有所增加，这导致医学图像处理的数量不断增加。传统技术无法满足大部分医学需求。目前，采用混沌递归对角模型的卷积神经网络（CNN）算法在医学图像处理中具有巨大优势，已成为大多数医院不可或缺的一部分。本文简要介绍了近年来医学科技的运用情况。主要采用CNN在混沌递归对角模型中的混合算法进行技术研究，并分析该技术在医学图像处理中的应用。CNN算法利用混沌递归对角线模型进行优化。结果表明，混沌递归对角模型可以改进传统神经网络的结构，提高原有CNN算法的效率和精度。然后根据CNN算法和优化CNN算法进行医学图像处理的应用研究和比较。实验结果表明，混沌递归对角线模型优化的CNN算法有助于医学图像自动处理和患者病情分析。