CN110929775B - Convolutional neural network weight optimization method for retinopathy classification - Google Patents
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Abstract
The invention relates to the field of medical information intelligent processing, in particular to a convolutional neural network weight optimization method for retinopathy classification. Firstly, acquiring a fundus image training set and a multi-lesion label corresponding to the fundus image training set; searching an optimal initial weight through a single swarm leaping algorithm, then constructing a convolution layer, a pooling layer and a full-link layer in a convolutional neural network, and taking the optimal initial weight as a parameter for the first forward propagation calculation; respectively carrying out cross entropy loss calculation on four predicted values of four pathological changes in retina and a true value, summing to obtain a loss value, judging whether the loss value is abnormal, if so, generating a frog group around a previous forward propagation weight, and searching for an optimal frog updating network weight; otherwise, updating the network weight by adopting a gradient descent algorithm; and finally optimizing the final weight. The invention can effectively improve the accuracy of fundus image multi-lesion detection and has stronger application value to retinal diseases and adjuvant therapy.
Description
Technical Field
The invention relates to the field of medical information intelligent processing, in particular to a convolutional neural network weight optimization method for retinopathy classification.
Background
Color fundus images are the most basic examination modality for diagnosing ophthalmic diseases. Meanwhile, the fundus images can enable people to discover various eye diseases such as glaucoma, optic neuritis, macular degeneration and the like as early as possible, and the treatment is convenient, timely and effective. Early diagnosis and timely treatment can effectively reduce the prevalence rate. However, because the number of Chinese population is huge and the number of ophthalmologists is relatively limited, and a large amount of time is consumed for diagnosing the ophthalmic diseases by only depending on the doctors, other methods for large-scale screening are urgently needed. The computer-aided diagnosis not only can greatly reduce the workload of doctors, but also has the advantages of objectivity, rapidness, accuracy and the like.
With the rapid increase of the amount of annotated data and the great improvement of the performance of the graphics processor unit in recent years, the research of the convolutional neural network is rapidly started, and the advantages in the field of image classification are also reflected. However, due to the complexity of the fundus image, the gradient descent algorithm is easy to fall into a local optimal solution by adopting the traditional convolutional neural network to carry out multi-label classification on the fundus image. Therefore, a new method is urgently needed to effectively deal with the complexity of the fundus image and improve the accuracy of detecting the retinopathy.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a convolutional neural network weight optimization method for retinopathy classification.
In order to achieve the purpose, the invention adopts the following technical scheme:
a convolutional neural network weight optimization method for retinopathy classification comprises the following steps:
step 1, inputting a fundus image training set and labels, wherein the training set is X ═ X 1 ，x 2 ，...，x n ) 1, 2, 3, labeled B ═ B 1 ，b 2 ，...，b n ) 1, 2, 3., label b corresponding to the fundus image i One-hot coding is carried out to obtain a true value y _ true i ；
Step 2, initializing the weight parameters of the convolutional neural network, generating m frogs by adopting standard normal distribution, sequencing according to adaptive values, and finding out the optimal frogs f b Worst and worst frog f w Continuously updating the worst frog position and reordering until the single frog leaping algorithm meets the convergence condition to obtain the global optimal frog f q A 1 is to f q As the initial weight of the convolutional neural network;
step 3, inputting the jth fundus image in the training set into a convolutional neural network forward propagation calculation model, performing multilayer convolution and pooling, finally performing two-layer full-connection calculation, and outputting to obtain the jth predicted value y _ predict j ；
Step 4, the jth true value y _ true is processed j And the j-th predicted value y _ predict j Performing softmax calculation for softmax (y _ predict) j ) Performing cross entropy loss calculation to obtain the jth loss value loss j ；
Step 5, judging the jth loss value loss j If the abnormal weight is judged, correcting the abnormal weight by adopting a single swarm leaping algorithm, and taking the corrected weight as a new weight of the network, otherwise, continuing to execute the next step;
step 6, judging whether the network reaches the end condition, if so, generating a frog cluster around the initial frog by taking the final weight of the network as the initial frog, and carrying out frog jump optimization to obtain the global optimal frog f qb The weight parameter is the weight parameter of the final training of the algorithm;
step 7, bringing the weight parameters after training into the networkIn the method, fundus images are input, multilayer convolution and pooling are carried out, finally two-layer full-connection calculation is carried out, and the predicted value y for detecting various pathological changes in retina is output r 。
Further, as a preferred technical solution of the present invention, the specific steps of step 2 are as follows:
step 2.1, determining the number m of frogs, and generating all frogs by adopting standard normal distribution, wherein the standard normal distribution formula is as follows:
In formula (1), μ represents a position parameter, σ represents a scale parameter, x represents a random variable, and each frog f comprises an ownership value parameter in the network;
2.2, constructing a convolutional neural network forward propagation calculation model, and randomly selecting a small number w of fundus images from the fundus image training set as reference images;
step 2.3, bringing the frogs without loss value calculation into a convolutional neural network forward propagation calculation model, and calculating the loss value of the frogs, wherein the loss value calculation function is a fitness function of a single group frog-leaping algorithm, and the loss calculation formula is as follows:
in the formula (2), p represents a network output value, t represents a true value, s represents the dimensionality of each group of pathological change labels, b represents the number of types of retinopathy needing to be detected simultaneously, and all frogs are sorted in an ascending order according to a fitness function to obtain the optimal frogs f b Worst and worst frog f w ；
And 2.4, updating the position of the worst frog through a position updating function, wherein the position updating function is as follows:
D＝(f b +f p -f w )×Rand(0，1.2) (3)
f new ＝f w +D (4)
in the formula (3), f p Representing an offset, the dimension of which is the same as the dimension of each frog; in the formula (4), f new Representing the updated frog; in the formula (5), f pi Denotes f p The value in the ith dimension;
and 2.5, judging whether the single frog leap algorithm meets the convergence condition, if so, stopping the algorithm, and taking the value of the optimal frog as the initial weight of the convolutional neural network, otherwise, turning to the step 2.3.
Further, as a preferred technical solution of the present invention, the specific steps of step 5 are as follows:
step 5.1, judging the loss value loss calculated at the j-1 th time j-1 Loss value loss from j-th calculation j Absolute value of the difference of | loss j-1 -loss j If I is larger than the threshold value epsilon, executing the next step, otherwise, not executing the leapfrog calculation;
step 5.2, taking the weight value in the j-1 th network as the initial frog w b Around w b The frog group is generated according to the following formula:
w ij ＝w bj +0.0001×Rand(-1，1)(1＜i≤c-1) (6)
in the formula (6), w ij Representing the value of the ith frog generated in the jth dimension, and c representing the total number of the frogs;
step 5.3, constructing a convolutional neural network forward propagation calculation model, and randomly selecting a small number w of fundus images from a training image library as reference images;
step 5.4, bringing the frogs with the loss values not calculated into a convolutional neural network forward propagation calculation model, calculating the loss values of the frogs and sequencing the frogs;
step 5.5, updating the worst frog position, calculating the updated loss value, and reordering the frog group;
step 5.6,Judging whether the single swarm frog-leaping algorithm meets the convergence condition, if so, ending the frog-leaping calculation, and obtaining the overall optimal frog f qb And (4) taking the corrected new weight value into the network, otherwise, turning to the step 5.4.
Further as a preferred technical solution of the present invention, the convolutional neural network forward propagation computation model in step 3 includes a convolutional layer, a pooling layer, and a fully connected layer.
Compared with the prior art, the convolutional neural network weight optimization method for classifying the retinopathy has the following technical effects by adopting the technical scheme:
the invention provides a convolutional neural network weight optimization method for retinopathy classification, which optimizes the initialization weight, the abnormal weight and the final weight of a convolutional neural network by adopting a single swarm frog-leaping algorithm, can effectively deal with the complexity of fundus images, and improves the detection accuracy of retinopathy.
The invention can search the optimal initial value of the network through the single swarm frog-leap algorithm, reduce the initial loss value of the convolutional neural network for classifying the multiple lesions of the fundus image, improve the network execution efficiency, correct the abnormal weight through the single swarm frog-leap algorithm, effectively avoid the convolutional neural network from falling into the local optimization, optimize the final weight, and effectively improve the classification accuracy. The method can further improve the detection efficiency of fundus image lesion, is applied to pathological detection in an electronic medical record system, and provides effective intelligent medical service for multi-level comprehensive intelligent decision support of human diseases.
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FIG. 1 is a schematic flow diagram of the present invention;
fig. 2 is a schematic diagram of an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, a convolutional neural network weight optimization method for retinopathy classification includes the following steps:
step 1, inputting a fundus image training set and a fundus image training setLabel, training set X ═ X (X) 1 ，x 2 ，...，x n ) 1, 2, 3, labeled B ═ B 1 ，b 2 ，...，b n ) 1, 2, 3., label b corresponding to the fundus image i One-hot coding is carried out to obtain a true value y _ true i ；
Step 2, initializing the weight parameters of the convolutional neural network, generating m frogs by adopting standard normal distribution, sequencing according to adaptive values, and finding out the optimal frogs f b Worst and worst frog f w Continuously updating the worst frog position and reordering until the single frog leaping algorithm meets the convergence condition to obtain the global optimal frog f q A 1 is to f q As the initial weight of the convolutional neural network;
step 3, inputting the jth fundus image in the training set into a convolutional neural network forward propagation calculation model, performing multilayer convolution and pooling, finally performing two-layer full-connection calculation, and outputting to obtain the jth predicted value y _ predict j ；
Step 4, the jth true value y _ true is processed j And the j-th predicted value y _ predict j Performing softmax calculation for softmax (y _ predict) j ) Performing cross entropy loss calculation to obtain the jth loss value loss j ；
Step 5, judging the jth loss value loss j If the abnormal weight is judged, correcting the abnormal weight by adopting a single swarm leaping algorithm, and taking the corrected weight as a new weight of the network, otherwise, continuing to execute the next step;
step 6, judging whether the network reaches the end condition, if so, generating a frog cluster around the initial frog by taking the final weight of the network as the initial frog, and carrying out frog jump optimization to obtain the global optimal frog f qb The weight parameter is the weight parameter of the final training of the algorithm;
step 7, the trained weight parameters are brought into a network, fundus images are input, two-layer full-connection calculation is finally carried out through multilayer convolution and pooling, and the predicted value y for detecting various pathological changes in retina is output r 。
The specific steps of step 2 are as follows:
step 2.1, determining the number m of frogs, and generating all frogs by adopting standard normal distribution, wherein the standard normal distribution formula is as follows:
in formula (1), μ represents a position parameter, σ represents a scale parameter, x represents a random variable, and each frog f comprises an ownership value parameter in the network;
2.2, constructing a convolutional neural network forward propagation calculation model, and randomly selecting a small number w of fundus images from the fundus image training set as reference images;
step 2.3, bringing the frogs without loss value calculation into a convolutional neural network forward propagation calculation model, and calculating the loss value of the frogs, wherein the loss value calculation function is a fitness function of a single group frog-leaping algorithm, and the loss calculation formula is as follows:
in the formula (2), p represents a network output value, t represents a true value, s represents the dimensionality of each group of pathological change labels, b represents the number of types of retinopathy needing to be detected simultaneously, and all frogs are sorted in an ascending order according to a fitness function to obtain the optimal frogs f b Worst and worst frog f w ；
And 2.4, updating the position of the worst frog through a position updating function, wherein the position updating function is as follows:
D＝(f b +f p -f w )×Rand(0，1.2) (3)
f new ＝f w +D (4)
in the formula (3), f p Representing an offset, the dimension of which is the same as the dimension of each frog; in the formula (4), f new Representing the updated frog; in the formula (5), f pi Denotes f p The value in the ith dimension;
and 2.5, judging whether the single frog leap algorithm meets the convergence condition, if so, stopping the algorithm, and taking the value of the optimal frog as the initial weight of the convolutional neural network, otherwise, turning to the step 2.3.
The specific steps of step 5 are as follows:
step 5.1, judging the loss value loss calculated at the j-1 th time j-1 Loss value loss from j-th calculation j Absolute value of the difference of | loss j-1 -loss j If the | is larger than the threshold epsilon, executing the next step, otherwise, not executing the frog leap calculation;
step 5.2, taking the weight value in the j-1 th network as the initial frog w b Around w b The frog group is generated according to the following formula:
w ij ＝w bj +0.0001×Rand(-1，1)(1＜i≤c-1) (6)
in the formula (6), w ij Representing the value of the ith frog generated in the jth dimension, and c representing the total number of the frogs;
step 5.3, constructing a convolutional neural network forward propagation calculation model, and randomly selecting a small number w of fundus images from a training image library as reference images;
step 5.4, bringing the frogs with the loss values not calculated into a convolutional neural network forward propagation calculation model, calculating the loss values of the frogs and sequencing the frogs;
step 5.5, updating the worst frog position, calculating the updated loss value, and reordering the frog group;
step 5.6, judging whether the single swarm frog-leaping algorithm meets the convergence condition, if so, ending the frog-leaping calculation, and obtaining the overall optimal frog f qb And (4) taking the corrected new weight value into the network, otherwise, turning to the step 5.4.
The convolutional neural network forward propagation calculation model in the step 3 comprises a convolutional layer, a pooling layer and a full-link layer.
Firstly, acquiring a fundus image training set and a multi-lesion label corresponding to the fundus image training set; searching an optimal initial weight through a single swarm leaping algorithm, then constructing a convolution layer, a pooling layer and a full-link layer in a convolutional neural network, and taking the optimal initial weight as a parameter for the first forward propagation calculation; respectively carrying out cross entropy loss calculation on four predicted values of four pathological changes in retina and a true value, summing to obtain a loss value, judging whether the loss value is abnormal, if so, generating a frog group around a previous forward propagation weight, and searching for an optimal frog updating network weight; otherwise, updating the network weight by adopting a gradient descent algorithm; and finally optimizing the final weight.
As shown in FIG. 2, in the concrete implementation, step 1, label b i Is a 1 × 4 array, and obtains a 1 × 8 true value y _ true through one-hot coding i ；
Step 2, initializing the weight parameters of the convolutional neural network, generating 15 frogs by adopting standard normal distribution, sequencing according to adaptive values, and finding out the optimal frogs f b Worst and worst frog f w Continuously updating the worst frog position and reordering until the algorithm meets the convergence condition to obtain the global optimum frog f q A 1 to f q As the initial weight of the convolutional neural network;
step 3, setting the jth sheet in the training set to be 500, 400, 3]The fundus image of (1) is input into the algorithm model, and the size of the convolution kernel of the first layer is [5, 5, 3, 32 ]]Offset is [32 ]]Obtaining a [1, 500, 400, 32 ] by the first layer convolution]Is pooled to obtain [1, 250, 200, 32 ]]Of a second layer convolution kernel size of [5, 5, 32, 64]Offset is of magnitude [64 ]]Obtaining a [1, 250, 200, 64 ] by the second layer convolution]Then pooling to obtain [1, 125, 100, 64 ]]The parameters of the last two fully-connected layers of the matrix are respectively one [ 125X 100X 64, 8 ]]And one [8, 8 ]]Of offset size [8 ]]Outputting to obtain the j-th predicted value y _ predict j Has a size of [1, 8 ]]；
Step 4, the jth true value y _ true is processed j And j predictionThe value y _ predict j Performing softmax calculation for softmax (y _ predict) j ) Performing cross entropy loss calculation to obtain the jth loss value loss j ；
Step 5, judging whether the loss value of the jth time is abnormal, if so, correcting the abnormal weight by adopting a single swarm leaping algorithm, taking the corrected weight as a new weight of the network, and otherwise, continuing to execute the next step;
Step 6, judging whether the network reaches the end condition, if so, taking the final weight of the network as an initial frog, generating a frog cluster around the initial frog, wherein the size of the frog cluster is 20, and carrying out frog jump optimization to obtain a global optimal frog f qb Namely, the weight parameter of the algorithm which is finally trained is obtained;
and 7, substituting the trained weight into a network, inputting an eye fundus image, performing multilayer convolution and pooling, finally performing two-layer full-connection calculation, and outputting a predicted value y for detecting various pathological changes in retina r Has a size of [1, 8 ]]。
The invention optimizes the initialization weight, the abnormal weight and the final weight of the convolutional neural network by adopting a single swarm frog-leaping algorithm, can effectively deal with the complexity of fundus images, and improves the accuracy of detecting retinopathy.
The invention can search the optimal initial value of the network through the single swarm frog-leap algorithm, reduce the initial loss value of the convolutional neural network for classifying the multiple lesions of the fundus image, improve the network execution efficiency, correct the abnormal weight through the single swarm frog-leap algorithm, effectively avoid the convolutional neural network from falling into the local optimization, optimize the final weight, and effectively improve the classification accuracy. The method can further improve the detection efficiency of fundus image lesion, is applied to pathological detection in an electronic medical record system, and provides effective intelligent medical service for multi-level comprehensive intelligent decision support of human diseases.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only illustrative of the present invention, and are not intended to limit the scope of the present invention, and any person skilled in the art should understand that equivalent changes and modifications made without departing from the concept and principle of the present invention should fall within the protection scope of the present invention.
Claims (2)
1. A convolutional neural network weight optimization method for retinopathy classification is characterized by comprising the following steps:
step 1, inputting a fundus image training set and a label, wherein the training set is X ═ X (X) 1 ,x 2 ,…,x n ) N is 1,2,3 …, and the label is B (B) 1 ,b 2 ,…,b n ) 1,2,3 …, label b corresponding to fundus image i One-hot coding is carried out to obtain a true value y _ true i ；
Step 2, initializing the weight parameters of the convolutional neural network, generating m frogs by adopting standard normal distribution, sequencing according to adaptive values, and finding out the optimal frogs f b Worst and worst frog f w Continuously updating the worst frog position and reordering until the single frog leaping algorithm meets the convergence condition to obtain the global optimal frog f q A 1 is to f q As the initial weight of the convolutional neural network; the method comprises the following specific steps:
Step 2.1, determining the number m of frogs, and generating all frogs by adopting standard normal distribution, wherein the standard normal distribution formula is as follows:
in formula (1), μ represents a position parameter, σ represents a scale parameter, x represents a random variable, and each frog f comprises an ownership value parameter in the network;
2.2, constructing a convolutional neural network forward propagation calculation model, and randomly selecting a small number w of fundus images from the fundus image training set as reference images;
step 2.3, bringing the frogs without loss value calculation into a convolutional neural network forward propagation calculation model, and calculating the loss value of the frogs, wherein the loss value calculation function is a fitness function of a single group frog-leaping algorithm, and the loss calculation formula is as follows:
in the formula (2), p represents a network output value, t represents a true value, s represents the dimensionality of each group of pathological change labels, b represents the number of types of retinopathy needing to be detected simultaneously, and all frogs are sorted in an ascending order according to a fitness function to obtain the optimal frogs f b Worst and worst frog f w ；
And 2.4, updating the position of the worst frog through a position updating function, wherein the position updating function is as follows:
D＝(f b +f p -f w )×Rand(0,1.2) (3)
f new ＝f w +D (4)
in the formula (3), f p Representing an offset, the dimension of which is the same as the dimension of each frog; in the formula (4), f new Representing the updated frog; in the formula (5), f pi Denotes f p The value in the ith dimension;
step 2.5, judging whether the single swarm frog-leaping algorithm meets the convergence condition, if so, stopping the algorithm, and taking the value of the optimal frog as the initial weight of the convolutional neural network, otherwise, turning to step 2.3;
step 3, inputting the jth fundus image in the training set into a convolutional neural network forward propagation calculation model, wherein the convolutional neural network forward propagation calculation model comprises a convolutional layer, a pooling layer and a full-link layer, performing two-layer full-link calculation through multi-layer convolution and pooling, and outputting to obtain the jth predicted value y _ predict j ；
Step 4, the jth true value y _ true is processed j And the j-th predicted value y _ predict j Performing softmax calculating softmax (y _ predict) j ) Performing cross entropy loss calculation to obtain the jth loss value loss j ；
Step 5, judging the jth loss value loss j If the abnormal weight is judged, correcting the abnormal weight by adopting a single swarm leaping algorithm, and taking the corrected weight as a new weight of the network, otherwise, continuing to execute the next step;
step 6, judging whether the network reaches the end condition, if so, generating a frog cluster around the initial frog by taking the final weight of the network as the initial frog, and carrying out frog jump optimization to obtain the global optimal frog f qb Namely, the weight parameters are the weight parameters finally trained by the algorithm;
step 7, the trained weight parameters are brought into a network, fundus images are input, two-layer full-connection calculation is finally carried out through multilayer convolution and pooling, and the predicted value y for detecting various pathological changes in retina is output r 。
2. The convolutional neural network weight optimization method for retinopathy classification as claimed in claim 1, wherein the specific steps of the step 5 are as follows:
step 5.1, judging the loss value loss calculated at the j-1 th time j-1 Loss value loss from j-th calculation j Absolute value of the difference of | loss j-1 -loss j If the | is larger than the threshold epsilon, executing the next step, otherwise, not executing the frog leap calculation;
step 5.2, taking the weight value in the j-1 th network as the initial frog w b Around w b The frog group is generated according to the following formula:
w ij ＝w bj +0.0001×Rand(-1,1)(1<i≤c-1) (6)
in the formula (6), w ij Representing the value of the ith frog generated in the jth dimension, and c representing the total number of the frogs;
step 5.3, constructing a convolutional neural network forward propagation calculation model, and randomly selecting a small number w of fundus images from a training image library as reference images;
step 5.4, bringing the frogs with the loss values not calculated into a convolutional neural network forward propagation calculation model, calculating the loss values of the frogs and sequencing the frogs;
Step 5.5, updating the worst frog position, calculating the updated loss value, and reordering the frog group;
step 5.6, judging whether the single swarm frog-leaping algorithm meets the convergence condition, if so, ending the frog-leaping calculation, and obtaining the overall optimal frog f qb And (4) taking the corrected new weight value into the network, otherwise, turning to the step 5.4.
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| CN109886143A (en) * | 2019-01-29 | 2019-06-14 | 上海鹰瞳医疗科技有限公司 | Multi-tag disaggregated model training method and equipment |
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| CN107403195A (en) * | 2017-07-28 | 2017-11-28 | 中南大学 | A kind of meteorologic parameter Intelligent Fusion processing method of carrying robot identification floor |
| CN109886143A (en) * | 2019-01-29 | 2019-06-14 | 上海鹰瞳医疗科技有限公司 | Multi-tag disaggregated model training method and equipment |
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