Disclosure of Invention
In view of the above problems, the present invention aims to provide a method and a system for automatic detection of kidney diseases based on machine learning.
The purpose of the invention is realized by the following technical scheme:
the kidney disease automatic detection method and system based on machine learning comprise a kidney image collection module, a kidney image management module, a kidney influence intelligent diagnosis module and a kidney disease classification module;
kidney image collection module: the system is used for collecting kidney disease images of various types of patients, wherein the sources of the kidney images comprise InterVar (site pathogenicity judgment), GeneReviews (disease database) and kidney images of patients in a hospital;
kidney image management module: the kidney image pre-storing unit is used for pre-storing various types of kidney images collected by the kidney image collecting module, screening the kidney images with kidney disease diagnosis labels through the kidney image updating unit, cleaning excessive noise, default or irrelevant images, and then updating a kidney image training set;
the intelligent kidney image diagnosis module comprises a kidney image processing unit and a kidney image database, wherein the kidney image processing unit extracts the disease characteristics of a kidney image by analyzing the kidney image with a kidney disease diagnosis label, a machine learning model is established by taking the kidney image updated by a kidney image updating unit as a training set, the kidney image to be judged is automatically detected for the disease, the trained kidney image with the kidney disease diagnosis label is stored in the kidney image database, and a distinguishable kidney image automatic disease detection model is generated so as to be convenient for next time without retraining and be directly used;
the kidney disease classification module further classifies the kidney diseases judged by the kidney image intelligent diagnosis module so as to ensure that a patient using the system can obtain a diagnosis and treatment scheme through self-checking kidney images.
Further, the kidney image collection module collects images from sources of InterVar (site pathogenicity assessment), GeneReviews (disease database) and kidney images of in-hospital patients, and collects the images to the sql database for online management.
Further, the kidney image updating unit screens the kidney images with the diagnosis labels of the kidney diseases, wherein the labels comprise calcific foci, density of renal pelvis of renal calyx, renal medulla, cyst and cancer embolus.
Furthermore, the kidney image processing unit performs image segmentation on the kidney image to be diagnosed by adopting a maximum entropy multi-threshold segmentation method, and determines an optimal combination threshold of the maximum entropy multi-threshold segmentation method adopted in the kidney image processing unit by adopting a particle swarm optimization.
Further, in the particle swarm algorithm, the particle swarm iteratively updates the solution of the particle in the following manner:
Vi(t+1)=ωVi(t)+ci1(t)r1(Pbesti(t)-Xi(t))+ci2(t)r2(Gbest(t)-Xi(t))
Xi(t+1)=Xi(t)+Vi(t+1)
in the formula, Xi(t +1) and Vi(t +1) denotes the solution and velocity of particle i at the (t +1) th iteration, X, respectivelyi(t) and Vi(t) respectively represents the solution and velocity of the particle i at the t-th iteration, ω represents an inertial weight factor, Pbesti(t) represents the historical optimal solution of particle i at the t-th iteration, Gbest (t) represents the global optimal solution of the particle swarm at the t-th iteration, r1And r2Representing a randomly generated random number between 0 and 1, ci1(t) cognitive learning factor for particle i at the t-th iteration, ci2(t) represents the social learning factor of particle i at the t-th iteration.
Further, setting a particle swarm to adjust the cognitive learning factor c of the particle i at the t-th iteration in the following wayi1(t) and social learning factor ci2Value of (t):
let omega
i(t) represents the local neighborhood of particle i at the t-th iteration, and Ω
i(t) is to solve X
i(t) a circular region with R (t) as the center and R (t) as the radius, R (t) is the local neighborhood radius of the particle swarm in the t iteration, and the value of R (t) is set as follows:
wherein R is
i(t) represents the local neighborhood radius of particle i at the t-th iteration, and
denotes the dissociation X of the particle population at the t-th iteration
i(t) a solution for the first nearest particle, c representing a given positive integer, and c < N, N representing the number of particles in the population; omega
i,best(t) represents the historical optimum neighborhood of particle i at the t-th iteration, and Ω
i,best(t) is to solve Pbest
i(t) a circular area with r (t) as the center and r (t) as the radius, r (t) is the historical optimal neighborhood radius of the particle swarm in the t iteration, and
wherein r is
i(t) represents the historical optimal neighborhood radius of particle i at the t-th iteration, an
In the formula,
denotes the dissociation of Pbest in the population at the t-th iteration
i(t) solution of the a-th particle;
defining a function qi(t) for detecting the optimum symmetry of the particle i in the tth iteration, q is setiThe formula for (t) is:
in the formula, qi(t) shows the optimum uniformity, J, of particle i at the tth iterationi(t) shows the local optimum symmetry, S, of particle i at the tth iterationi(t) represents the historical goodness of the particle i at the tth iteration, Ji(t) and SiThe values of (t) are:
in the formula, X
j(t) represents the solution of particle j at the t-th iteration, X
b(t) represents the solution of particle b at the t-th iteration, and X
j(t)≠X
b(t),X
k(t) represents the solution of particle k at the t-th iteration, X
o(t) watchShows the solution of particle o at the t-th iteration, and X
k(t)≠X
o(t),λ
i,j(t) is a determination function for the optimization between particle i and particle j at the t-th iteration, let f
i(t) and f
j(t) represents the fitness function values of particle i and particle j at the t-th iteration respectively, then
λ
i,b(t) is a determination function for the optimization between particle i and particle b at the t-th iteration, let f
b(t) represents the fitness function value of particle b at the t-th iteration, then
m
i,best(t) represents the historical optimum neighborhood Ω
i,bestThe number of particles in (t);
when q isi(t)<Hq1(t) or qi(t)>Hq2At (t), then ci1(t) and ci2The values of (t) were adjusted to:
when H is presentq1(t)≤qi(t)≤Hq2At (t), then ci1(t) and ci2The values of (t) were adjusted to:
in the above formula, α and β are two constants for controlling c
i1(t) and c
i2(t) range of,H
q1(t) is the lower bound of the global optimization symmetry of the particle swarm at the t-th iteration, and
H
q2(t) is the upper limit value of the global optimization symmetry of the particle swarm in the t-th iteration, and
wherein,
means representing the optimum symmetry of the particles in the population at the t-th iteration, an
T
maxGiven a maximum number of iterations.
Furthermore, the kidney diseases judged by the intelligent kidney image diagnosis module are further classified by utilizing a back propagation algorithm so as to ensure that a patient using the system can obtain a diagnosis and treatment scheme through the self-checking kidney image, and the method specifically comprises the following steps:
firstly, building an M-layer neural network, and for the output of the (M +1) th-layer network, the following steps are provided:
am+1=fm+1(Wm+1am+bm+1),m=0,1,…,M-1
where M is the number of network layers, a is the output of the neural network, i.e., the class of renal disease, there is a0P and aM,am+1Is the output result of the (m +1) th layer neural network, amIs the output result of the mth layer neural network, fm+1Is the excitation function of the neural network of layer m +1, Wm+1Is the weight of the (m +1) th neural network, bm+1Is the bias of the (m +1) th layer neural network, assuming that for each type of input p of the kidney disease characteristics, there is a corresponding kidney disease type output t, the following correspondence can be established:
{p1,t1},{p2,t2},…,{pq,tq},…,{pQ,tQ}
wherein p is
1Is the input of the 1 st kidney image feature, t
1Is the expected output, p, for the 1 st class
2Is the input of the 2 nd kidney image feature, t
2Is the expected output, p, for the 2 nd class
qIs the input of the qth renal image feature, t
qIs the expected output, p, corresponding to the qth class
QIs the input of the Q-th renal image feature, t
QIs the desired output for the qth class, the algorithm will adjust the network parameters to minimize the mean squared error: f (x) E [ (t-a)
2]=E[(t-a)
T(t-a)]Let E-t-a denote the error of the desired output from the predicted output, where F denotes the mean square error, x denotes the arguments of F, which in this case include t and a, E denotes the mathematical expectation, (t-a)
TTranspose of the representation error by
Instead of f (x), the mean square error is calculated approximately:
wherein k is the kth iteration, and the steepest descent method of the approximate mean square error is as follows:
wherein a is a learning speed of the learning,
represents the weight of the ith element to the jth element at the (k +1) th iteration of the mth layer neural network,
representing the weight of the ith element to the jth element under the kth iteration of the mth layer neural network, and defining a chain rule as follows:
if f (n) is e ^ n and n is 2w, then f (n) (w) is e ^ 2w, have
Then use the chain rule
And
can be written as:
for the net input n of the mth layer, there are:
wherein s represents sensitivity, s
m-1Representing the sensitivity of the (m-1) th layer neural network,
representing the net input of the ith element in an m-layer neural network,
represents the output of the jth element in an m-1 layer neural network,
represents the bias of the ith element in an m-layer neural network, thus:
if defined:
then
And
can be simplified as follows:
expressed as:
wherein,
representing the bias of the ith element in the (k +1) th iteration of the m-layer neural network, the matrix form can be expressed as:
Wn(k+1)=wm(k)-αsm(am-1)T
bm(k+1)=bm(k)-αsm
wherein, Wm(k +1) represents wm(k +1) matrix form, i.e. set of weights after k +1 iterations of the m-layer neural network, where smComprises the following steps:
wherein,
the net input to the mth layer representing the approximation error is partial,
1 st net input to m-th layer representing approximation errorThe partial derivatives of the light beams are deflected,
the 2 nd net input representing the approximation error to the mth layer is partial-derivative,
s-th layer representing approximation error pair
mThe net inputs are subjected to partial derivation, and the Jacobian matrix is recorded as:
wherein,
representing the partial derivative of the net input to the layer m +1 neural network to the net input to the layer m neural network,
represents the partial derivative of the 1 st net input of the (m +1) th layer neural network to the 1 st net input of the m' th layer neural network,
representing the partial derivative of the 1 st net input of the (m +1) th layer neural network to the 2 nd net input of the m' th layer neural network,
represents the s of the 1 st net input of the m +1 st layer neural network to the m layer neural network
mThe partial derivative of the individual net inputs,
represents the partial derivative of the 2 nd net input of the m +1 th layer neural network to the 1 st net input of the m layer neural network,
representing the net 2 input of the (m +1) th neural network to the net 2 input of the (m) th neural networkThe partial derivatives of the light beams are reflected by the light beam,
represents the s < nd > net input of the (m +1) th layer neural network to the m < th > neural network
mThe partial derivative of the individual net inputs,
s representing the (m +1) th neural network
m+1The partial derivative of the net input to the net input of the mth layer neural network,
s represents the (m +1) th layer neural network
m+1Partial derivatives of the 1 st net input to the mth layer neural network,
s representing the (m +1) th neural network
m+1S of net input to mth layer neural network
mThe partial derivative of the individual net inputs,
s representing the (m +1) th neural network
m+1S of net input to mth layer neural network
mPartial derivatives of the individual net inputs, taking into account the i, j elements of the matrix:
wherein:
the jacobian matrix can then be written as:
wherein:
F
m(n
m) Represents the net input of the mth layer neural network as n
mThe objective function of the time of day,
represents a net input to the mth layer neural network of
The objective function of the time of day,
represents a net input to the mth layer neural network of
The objective function of the time of day,
represents a net input to the mth layer neural network of
And (3) writing a sensitivity recurrence relation formula by a chain rule of a matrix form according to the time target function:
further, the optimal training weight is obtained by utilizing a back propagation algorithm and a chain method, and the disease information of the patient is input into a trained machine learning model, so that whether the patient has calcified foci, density of renal pelvis, renal medullary, cyst and cancer embolism diseases or not can be automatically detected.
Further, to the machine learning model who establishes, realize the kidney image automated inspection disease to needs are differentiated, will train good kidney image that has kidney disease diagnosis label and keep to kidney image database, generate the kidney image automated inspection disease's that can differentiate model and need not retraining once more in order to make things convenient for next time, directly use, kidney disease classification module is further categorised to the kidney disease that kidney image intelligent diagnosis module differentiateed to guarantee that the patient that uses this system can obtain diagnosis and treatment scheme through self-checking kidney image.
The beneficial effects of the invention are as follows:
(1) the artificial intelligence technology is applied to kidney image analysis, tumor diagnosis models corresponding to human body parts based on kidney images are established, tumor diagnosis can be effectively carried out on the human body parts, and the functions of automatic analysis of the kidney images and medical tumor diagnosis assisting doctors are realized;
(2) the method comprises the steps of performing image segmentation on a kidney image to be diagnosed by adopting a maximum entropy multi-threshold segmentation method so as to obtain a human body part image contained in the kidney image to be diagnosed, and determining an optimal combined threshold of the maximum entropy multi-threshold segmentation method by adopting a particle swarm algorithm, so that the accuracy of kidney image segmentation is improved, and a foundation is laid for the subsequent tumor diagnosis;
(3) the particle swarm optimization is set, the iterative updating mode of the particle swarm is adjusted by adopting the cognitive learning factor and the social learning factor which change in a self-adaptive manner, so that the global exploration and the local exploitation of the particle swarm optimization are balanced, the optimizing precision and the convergence speed of the particle swarm optimization are improved, and the optimal combination threshold determined by adopting the particle swarm optimization has higher accuracy.
Detailed Description
The kidney disease automatic detection method and system based on machine learning comprise a kidney image collection module, a kidney image management module, a kidney influence intelligent diagnosis module and a kidney disease classification module;
kidney image collection module: for collecting images of various types of kidney diseases of patients, the sources of the kidney images include InterVar (site pathogenicity assessment), GeneRereviews (disease database), and images of the kidneys of patients in hospital.
Kidney image management module: the kidney image pre-storing unit is used for pre-storing various types of kidney images collected by the kidney image collecting module, screening the kidney images with kidney disease diagnosis labels through the kidney image updating unit, cleaning excessive noise, default or irrelevant images, and then updating a kidney image training set;
the intelligent kidney image diagnosis module comprises a kidney image processing unit and a kidney image database, wherein the kidney image processing unit extracts the disease characteristics of a kidney image by analyzing the kidney image with a kidney disease diagnosis label, a machine learning model is established by taking the kidney image updated by a kidney image updating unit as a training set, the kidney image to be judged is automatically detected for the disease, the trained kidney image with the kidney disease diagnosis label is stored in the kidney image database, and a distinguishable kidney image automatic disease detection model is generated so as to be convenient for next time without retraining and be directly used;
the kidney disease classification module further classifies the kidney diseases judged by the kidney image intelligent diagnosis module so as to ensure that a patient using the system can obtain a diagnosis and treatment scheme through self-checking kidney images.
Specifically, the kidney image collection module collects images from the sources of interavar (site pathogenicity assessment), GeneReviews (disease database) and the kidney images of the patients in the hospital, and collects the images to the sql database for online management.
Specifically, the kidney image updating unit screens kidney images with kidney disease diagnosis labels, wherein the labels comprise calcific foci, density of renal calyx and renal pelvis, renal medulla, cyst and cancer embolus.
Preferably, the medical image processing unit performs image segmentation on the medical image to be diagnosed by using a maximum entropy multi-threshold segmentation method, and determines an optimal combination threshold of the maximum entropy multi-threshold segmentation method used in the medical image processing unit by using a particle swarm algorithm.
Preferably, the fitness function of the particle swarm is set to be the maximum entropy, and the larger the fitness function of the particles in the particle swarm is, the better the solution obtained by particle optimization is.
Specifically, in the embodiment, the medical image to be diagnosed is subjected to image segmentation by using the maximum entropy multi-threshold segmentation method, so that a human body part image contained in the medical image to be diagnosed is obtained, and the optimal combined threshold of the maximum entropy multi-threshold segmentation method is determined by using the particle swarm algorithm, so that the accuracy of medical image segmentation is improved, and a foundation is laid for the subsequent tumor diagnosis.
Preferably, in the particle swarm algorithm, the particle swarm iteratively updates the solution of the particle in the following manner:
Vi(t+1)=ωVi(t)+ci1(t)r1(Pbesti(t)-Xi(t))+ci2(t)r2(Gbest(t)-Xi(t))
Xi(t+1)=Xi(t)+Vi(t+1)
in the formula, Xi(t +1) and Vi(t +1) denotes the solution and velocity, X, of particle i at the (t +1) th iteration, respectivelyi(t) and Vi(t) respectively represents the solution and velocity of particle i at the t-th iteration, ω represents an inertial weight factor, Pbesti(t) represents the historical optimal solution of particle i at the t-th iteration, Gbest (t) represents the global optimal solution of the particle swarm at the t-th iteration, r1And r2Representing a randomly generated random number between 0 and 1, ci1(t) cognitive learning factor for particle i at the t-th iteration, ci2(t) represents the social learning factor of particle i at the t-th iteration.
Preferably, the particle swarm is set to adjust the cognitive learning factor c of the particle i at the t-th iteration in the following wayi1(t) and social learning factor ci2The value of (t):
let omega
i(t) represents the local neighborhood of particle i at the t-th iteration, and Ω
i(t) is to solve X
i(t) A circular area with r (t) as a center and r (t) as a radius, wherein R (t) is a local neighborhood radius of the particle swarm in the t iteration, and the value of R (t) is set as follows:
wherein R is
i(t) represents the local neighborhood radius of particle i at the t-th iteration, and
denotes the dissociation X of the particle population at the t-th iteration
i(t) a solution for the first nearest particle, c represents a given positive integer, and c < N, where c may take a value of 10 and N represents the number of particles in the population; omega
i,best(t) represents the historical optimal neighborhood of particle i at the t-th iteration, and Ω
i,best(t) is to solve Pbest
i(t) a circular area with r (t) as the center and r (t) as the radius, r (t) is the historical optimal neighborhood radius of the particle swarm in the t iteration, and
wherein r is
i(t) represents the historical optimal neighborhood radius of particle i at the t-th iteration, an
In the formula,
denotes the dissociation of Pbest in the population at the t-th iteration
i(t) a solution for the particle at a-th;
defining a function qi(t) for detecting the optimum symmetry of the particle i in the tth iteration, q is setiThe formula for (t) is:
in the formula, qi(t) represents the optimized uniformity of particle i at the t-th iteration, Ji(t) denotes the local optimum symmetry, S, of particle i at the t-th iterationi(t) represents the historical goodness of the particle i at the tth iteration, Ji(t) and SiThe values of (t) are:
in the formula, X
j(t) represents the solution of particle j at the t-th iteration, X
b(t) represents the solution of particle b at the t-th iteration, and X
j(t)≠X
b(t),X
k(t) represents the solution of particle k at the t-th iteration, X
o(t) represents the solution of particle o at the t-th iteration, and X
k(t)≠X
o(t),λ
i,j(t) is a determination function for the optimization between particle i and particle j at the t-th iteration, let f
i(t) and f
j(t) the fitness function values of particle i and particle j at the t-th iteration, respectively, are then
λ
i,b(t) is a determination function for the optimization between particle i and particle b at the t-th iteration, let f
b(t) represents the fitness function value of particle b at the t-th iteration, then
m
i,best(t) represents the historical optimum neighborhood Ω
i,bestThe number of particles in (t);
when q isi(t)<Hq1(t) or qi(t)>Hq2At (t), then ci1(t) and ci2The values of (t) were adjusted to:
when H is presentq1(t)≤qi(t)≤Hq2At (t), then ci1(t) and ci2The values of (t) were adjusted to:
in the above formula, α and β are two constants for controlling c
i1(t) and c
i2(t) wherein α may take the value of 1, β may take the value of 0.5, H
q1(t) is the lower bound of the global optimization symmetry of the particle swarm at the t-th iteration, and
H
q2(t) is the upper limit value of the global optimizing uniformity of the particle swarm in the t iteration, and
wherein,
means representing the optimum symmetry of the particles in the population at the t-th iteration, an
T
maxGiven a maximum number of iterations.
Specifically, the kidney diseases judged by the intelligent kidney image diagnosis module are further classified by utilizing a back propagation algorithm so as to ensure that a patient using the system can obtain a diagnosis and treatment scheme through a self-checking kidney image, and the method comprises the following specific steps:
firstly, an M-layer neural network is built, and for the output of the (M +1) -th layer network, the following steps are provided:
am+1=fm+1(Wm+1am+bm+1),m=0,1,…,M-1
wherein M is the number of network layers, a is the output result of the neural network, namely the type of the kidney disease, and a exists0P and aM,am+1Is the output result of the (m +1) th layer neural network, amIs the output result of the mth layer neural network, fm+1Is the excitation function of the (m +1) th layer neural network, Wm+1Is the weight of the (m +1) th layer neural network, bm+1The bias of the (m +1) th layer neural network, assuming that there is a corresponding kidney disease type output t for each type of input p of the kidney disease characteristics, the following correspondence can be established:
{p1,t1},{p2,t2},…,{pq,tq},…,{pQ,tQ}
wherein p is
1Is the input of the 1 st kidney image feature, t
1Is the expected output, p, for the 1 st class
2Is the input of the 2 nd kidney image feature, t
2Is the expected output, p, for the 2 nd class
qIs the input of the qth renal image feature, t
qIs the expected output, p, for the qth class
QIs the input of the Q-th kidney image feature, t
QIs the desired output for the qth class, the algorithm will adjust the network parameters to minimize the mean squared error: f (x) E [ (t-a)
2]=E[(t-a)
T(t-a)]Let E-t-a denote the error of the desired output from the predicted output, where F denotes the mean square error, x denotes the arguments of F, which in this case include t and a, E denotes the mathematical expectation, (t-a)
TTranspose of the representation error by
Instead of f (x), the mean square error is calculated approximately:
wherein k is the kth iteration, and the steepest descent method of the approximate mean square error is as follows:
wherein a is a learning speed of the learning,
represents the weight of the ith element to the jth element at the (k +1) th iteration of the mth layer neural network,
and (3) representing the weight of the ith element to the jth element under the kth iteration of the mth layer neural network, and defining a chain rule as follows:
if f (n) is e ^ n and n is 2w, then f (n) (w) is e ^ {2w }, there are
Then use the chain rule
And
can be written as:
for the net input n of the mth layer, there are:
wherein s represents sensitivity, s
m-1To representThe sensitivity of the (m-1) th layer neural network,
representing the net input of the ith element in an m-layer neural network,
represents the output of the jth element in an m-1 layer neural network,
represents the bias of the ith element in an m-layer neural network, thus:
if defined:
then
And
can be simplified as follows:
expressed as:
wherein,
representing the bias of the ith element in the (k +1) th iteration of the m-layer neural network, the matrix form can be expressed as:
Wm(k+1)=wm(k)-αsm(am-1)T
bm(k+1)=bm(k)-αsm
wherein, Wm(k +1) represents wmMatrix form of (k +1), i.e. set of weights after k +1 th iteration of the m-layer neural network, where smComprises the following steps:
wherein,
the net input to the mth layer representing the approximation error is partial,
the 1 st net input representing the approximation error to the mth layer is partial-derivative,
the 2 nd net input representing the approximation error to the mth layer is partial,
s-th layer representing approximation error pair
mThe net inputs are subjected to partial derivation, and the Jacobian matrix is recorded as:
wherein,
representing the partial derivative of the net input to the layer m +1 neural network to the net input to the layer m neural network,
representing the 1 st Net input of the m +1 st layer neural network to the 1 st Net input of the m layer neural networkThe partial derivatives of (a) are,
representing the partial derivative of the 1 st net input of the (m +1) th layer neural network to the 2 nd net input of the m' th layer neural network,
represents the s of the 1 st net input of the m +1 st layer neural network to the m layer neural network
mThe partial derivative of the individual net inputs,
represents the partial derivative of the 2 nd net input of the m +1 th layer neural network to the 1 st net input of the m layer neural network,
represents the partial derivative of the 2 nd net input of the (m +1) th layer neural network to the 2 nd net input of the m layer neural network,
represents the s < nd > net input of the (m +1) th layer neural network to the m < th > neural network
mThe partial derivative of the individual net inputs,
s represents the (m +1) th layer neural network
m+1The partial derivative of this net input to the net input of the mth layer neural network,
s representing the (m +1) th neural network
m+1Partial derivatives of the 1 st net input to the mth layer neural network,
s representing the (m +1) th neural network
m+1S of net input to mth layer neural network
mThe partial derivative of the individual net inputs,
s represents the (m +1) th layer neural network
m+1S of net input to mth layer neural network
mPartial derivatives of the individual net inputs, consider the i, j elements of the matrix:
wherein:
then the jacobian matrix can be written as:
wherein:
F
m(n
m) Represents the net input of the mth layer neural network as n
mThe objective function of the time-of-day,
represents a net input to the mth layer neural network of
The objective function of the time of day,
represents a net input to the mth layer neural network of
The objective function of the time of day,
represents a net input to the mth layer neural network of
The objective function of time is then in the form of a matrixThe chain rule writes a recurrence relation of sensitivity:
specifically, the optimal training weight is obtained by using a back propagation algorithm and a chain method, and the disease information of the patient is input into a trained machine learning model, so that whether the patient has calcified foci, density of renal calyx and renal pelvis, renal medulla, cyst and cancer embolus diseases or not can be automatically detected.
Specifically, to the machine learning model who establishes, realize the kidney image automated inspection disease to needs are differentiated, will train good kidney image that has kidney disease diagnosis label and keep to kidney image database, generate the kidney image automated inspection disease's that can differentiate model and need not retraining once more in order to make things convenient for next time, directly use, kidney disease classification module is further categorised to the kidney disease that kidney image intelligent diagnosis module differentiateed to the patient of guaranteeing to use this system can obtain diagnosis and treatment scheme through self-checking kidney image.
Specifically, when the particle swarm algorithm is applied to determine the optimal combined threshold of the maximum entropy multi-threshold segmentation method, the diversity of the particle swarm is poor easily, so that the phenomenon of early maturity occurs, or the convergence speed of the particle swarm is slowed down while the diversity of the particle swarm is enhanced, so that the problem of resource waste occurs. That is, in the particle swarm optimization, increasing the diversity of the particles and increasing the convergence rate of the particles are contradictory, and the balance between the two is difficult. Considering that in the particle swarm algorithm, the cognitive learning factor maintains a larger value, which is helpful for searching particles in a large range, so as to improve the diversity of the particle swarm, but results in a slower convergence rate of the algorithm, while the social learning factor maintains a larger value, which is helpful for learning social experiences, so that the search result quickly converges to the vicinity of the optimal solution, so as to improve the convergence rate of the algorithm, but the search space capacity is weaker, therefore, the embodiment balances the global exploration and the local exploitation of the particle swarm algorithm by adopting the cognitive learning factor and the social learning factor value which are adaptively changed, thereby playing roles of improving the diversity of the particle swarm and improving the convergence rate. In the iterative updating formula of the particles, the cognitive part reflects the self-learning ability of the particles in exploration and reflects the following trend of the particles to the best characteristics of the particles, and the social part reflects the ability of the particles to learn from the outside and reflects the group behaviors of cooperative cooperation and knowledge sharing among the particles. And the cognitive learning factor and the social learning factor respectively determine the contribution degree of the cognitive part and the social part in the particle iterative updating process. Considering that when the particles in the particle swarm optimize the current solution space more uniformly, the diversity of the particle swarm is better, and the globally optimal solution is found with a higher probability, therefore, the embodiment defines the optimization symmetry to be used for measuring the uniformity of the particle in the solution space where the particle is located, when the uniformity of the particle in the solution space where the particle is located is measured, not only the uniformity of the solution space where the particle is located is measured by the local optimization symmetry in the optimization symmetry, but also the uniformity of the solution space where the particle is located is measured by the historical optimization symmetry in the optimization symmetry, so that when the value of the cognitive learning factor of the particle is increased according to the optimization symmetry of the particle, the cognitive part of the particle can better increase the diversity of the particle. In the particle swarm, when the individual optimizing symmetry of the particle is smaller or larger than the global optimizing symmetry, it indicates that the particle is not sufficiently learning the solution space it is in the current exploration compared to the global, thus, increasing the cognitive learning factor of the particle, decreasing the social learning factor of the particle, namely, the contribution degree of the cognitive part in the iterative updating process is enhanced, so that the diversity of the algorithm is improved, when the optimizing uniformity of the particle individual is moderate compared with the global optimizing uniformity, it indicates that the particle has learned the solution space in which it is located more fully than globally in the current exploration, thus, decreasing the cognitive learning factor of the particle, increasing the social learning factor of the particle, namely, the contribution degree of the social part of the algorithm in the iterative updating process is enhanced, so that the convergence speed of the algorithm is improved. The convergence rate of the particle swarm algorithm can be improved under the condition that the diversity of the particle swarm in the current solution space is better ensured through the cognitive learning factor and the social learning factor regulated by the optimizing uniformity, and therefore the optimizing precision of the particle swarm is improved.
The beneficial effects of the invention are as follows: the artificial intelligence technology is applied to kidney image analysis, tumor diagnosis models corresponding to human body parts based on kidney images are established, tumor diagnosis can be effectively carried out on the human body parts, and the functions of automatic analysis of the kidney images and medical tumor diagnosis assisting doctors are realized; the method comprises the steps of performing image segmentation on a kidney image to be diagnosed by adopting a maximum entropy multi-threshold segmentation method so as to obtain a human body part image contained in the kidney image to be diagnosed, and determining an optimal combined threshold of the maximum entropy multi-threshold segmentation method by adopting a particle swarm algorithm, so that the accuracy of kidney image segmentation is improved, and a foundation is laid for the subsequent tumor diagnosis; the particle swarm optimization is set, the iterative updating mode of the particle swarm is adjusted by adopting the cognitive learning factor and the social learning factor which change in a self-adaptive manner, so that the global exploration and the local exploitation of the particle swarm optimization are balanced, the optimizing precision and the convergence speed of the particle swarm optimization are improved, and the optimal combination threshold determined by adopting the particle swarm optimization has higher accuracy.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.