CN107909095A - A kind of image-recognizing method based on deep learning - Google Patents
A kind of image-recognizing method based on deep learning Download PDFInfo
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Abstract
The invention discloses a kind of image-recognizing method based on deep learning, belong to machine learning techniques field.This method shows good deep learning model VGG models in image processing field by improving, and full articulamentum is replaced using convolutional layer；Deep learning feature extraction is carried out to image, then the deep learning feature extracted, is trained by support vector machines (SVM), finally carries out Classification and Identification.The present invention proves that image-recognizing method of the invention can be effectively according to different criteria for classifications, and to judge the medical image generic of input, while the invention can greatly reduce calculating cost, to aid in diagnosis disease by the identification to medical image.Method using the present invention, can aid in diagnosis disease from objective angle, meet the diagnosis requirement of doctor, diagnosis efficiency is improved, so as to effectively reduce misdiagnosis rate.
Description
Technical field
Technical field belonging to the present invention is art of image analysis, is specially a kind of image recognition side based on deep learning
Method.
Background technology
Machine learning method is widely used in graphical analysis, by the data-oriented collection training pattern complete new data
On particular task, such as classification, identification and segmentation etc..Common algorithm has support vector machines (SVM), Hidden Markov
(HMM) and artificial neural network etc..However, traditional machine learning algorithm needs to utilize priori people from initial data
Work extracts feature, so that training pattern.Since Feature Selection difficulty is larger, there may be over-fitting problem, generalization ability for model
It is difficult to ensure that；On the other hand, conventional model is difficult in adapt to large-scale dataset, model poor expandability.
Deep learning is a new field in machine learning research, its motivation, which is to establish, simulates human brain is divided
Analysis study.Deep learning is a kind of data driven type model, can simulate human brain vision mechanism automatically learn it is each to data
The abstract characteristics of level, so as to preferably reflect the substantive characteristics of data.Good result of the deep learning model in every field
The upsurge for carrying out data mining and analysis using the technology in more areas is triggered, has also caused in medicine and biological cognitive domain
Pay attention to.Deep learning has begun to set foot in the lesion classification of medical image, segmentation, identification and cerebral function research etc. at present
Aspect.Deep learning venture company Enlitic develops the cancer detection system based on deep learning, the lung on chest CT image
Cancer recall rate exceedes doctor.IBM proposes Watson for Oncology, by learning mass data and experience, so as to analyze disease
The medical information of people, helps doctor to formulate reliable medical scheme.The Google subsidiaries DeepMind for developing AlphaGo is near
Day announces DeepMind Health projects, and effective health care science and technology is further developed using deep learning.
In place of the deficiencies in the prior art：
(1) manual extraction is needed to be adapted to the feature of medical image, but it is suitable special for different medical image extractions
Sign requires a great deal of time, and needs the experience of substantial amounts of association area.
(2) in order to obtain more preferable deep learning model, it is necessary to which substantial amounts of training data, data volume is too small may to cause mould
The over-fitting of type.Therefore a kind of method of good EDS extended data set is proposed in the present invention.Designed with reference to the present invention
Frame, can obtain preferable result on small-scale data set
The content of the invention
Present invention aims to use convolutional neural networks, the deep learning model of training image, extracts the depth of image
Spend learning characteristic and be built into database, Classification and Identification is carried out to feature finally by SVM, each process includes several and walks
Suddenly, comprise the following steps that：
Step 1, gather and expand image data set：Limited image is pre-processed, the sample of image is expanded
Fill；
Step 2, convolutional neural networks are designed：The convolutional neural networks model is on the basis of existing VGG-16 models
It is upper to replace full connection layer building using convolutional layer；
Step 3, training convolutional neural networks：Using back-propagation algorithm and stochastic gradient descent method, according to preceding to biography
The size for the loss values broadcast, to carry out the weight that backpropagation iteration updates each layer, until the loss values of model are intended to receive
When holding back, deconditioning model, obtains deep learning model；
Step 4, the feature of image is extracted：Every piece image in data set is input to the deep learning described in step 3
In model, for the image of input, in the full convolutional layer extraction deep learning feature of the layer second from the bottom of image；
Step 5, image is identified：For giving any one image to be identified, trained deep learning mould is input to
In type, the deep learning feature of sample is extracted, effectively differentiates which classification the image belongs to by method trained twice.
Further, the method for collection and the expansion of data set includes at least one of following methods in the step 1：
Mirror image switch：Original image is subjected to left and right reversion, by 2 times of data extending；
Add salt-pepper noise：Some salt-pepper noises will be added in original image, by 2 times of data extending；
Segmentation figure：Target area in artwork is split, other regions are replaced with 0, by 2 times of data extending.
Add illumination：Image is rotated by 90 °, 180 degree, 270 degree, by 3 times of data extending.
Further, the convolutional neural networks model of the step 2 is by input layer, hidden layer, output layer composition：
The input layer, by the data set obtained after expansion, data set is converted into by the image that step 1 proposes
Input of the lmdb forms as the model；
The hidden layer includes convolutional layer and pond layer；Convolution kernel is sized to 1*1 in the first layer of the convolutional layer,
Full connection is replaced using full convolution in layer second from the bottom, characteristic dimension is reduced to the half connected entirely.The pond layer uses
The convolution kernel of 2*2, is applied in combination in first five layer and convolutional layer of the model；
The output layer is connected with last full convolutional layer, the classification number phase of the dimension of output and image to be identified
Deng.
Further, the method that transfer learning is used during step 3 training convolutional neural networks, will be trained in advance
The weight of VGG models is migrated to the convolutional neural networks model.
Further, the training convolutional neural networks described in the step 3 further include：In the caffe frames of Ubuntu systems
Under, view data is divided into training set, training set label, test set, test set label.
Further, the step 4 further includes：Each sample in data set is corresponded into a row vector, and is sticked pair
The label answered, is built into the database of a deep learning feature.
Further, the dimension of the deep learning feature is 2048 dimensions.
Further, the training twice described in the step 5 is respectively：Training for the first time is the side by deep learning
Method, extracts an effective deep learning feature；Second of training is by the deep learning data having had been built up in step 4
Storehouse, by the use of SVM methods using the data in database and label as the training set of SVM, the depth obtained further according to arbitrary sample
Spend learning characteristic, the test set as SVM.
Beneficial effects of the present invention：
(1) test can be automatically identified according to the classification of trained deep learning model and SVM in the present invention
The classification of sample, without inputting any parameter.
(2) by deep learning and the dual training of SVM, the accuracy rate of image recognition can be greatly improved, with more reality
The property used.Apply in medical image recognition, can quickly provide identification image as a result, help diagnosis lesion.
Brief description of the drawings
Fig. 1 is the flow chart that the present invention is implemented.
Fig. 2 is the structure chart of deep learning network.
Embodiment
The invention will be further described with specific embodiment below in conjunction with the accompanying drawings.The present invention is identified as with medical image
Embodiment illustrates, but the present invention is not limited thereto.
As shown in Figure 1, method proposed by the present invention includes the following steps：
Step 1, the collection and expansion of medical images data sets
The purpose of collection and the expansion of medical image be in order to expand the sample size of deep learning training set, prevent due to
Training data is smaller to cause model over-fitting to occur.Mainly in the following ways：
Traditional medical image amount is less, and training deep learning model needs substantial amounts of training sample.Therefore this hair
It is bright by being pre-processed to limited medical image, to expand the sample of medical image.The medical image of the present invention
Collection and the purpose of expansion be to increase the sample size of deep learning training set, prevent since training data is less, can
Model over-fitting can be caused to occur.The collection and expansion of the data set mainly use following methods：
Mirror image switch：Original image is subjected to left and right reversion, by 2 times of data extending.
Add salt-pepper noise：Some salt-pepper noises will be added in original image, by 2 times of data extending.
Segmentation figure：Target area in artwork is split, other regions are replaced with 0, by 2 times of data extending.
Illumination is added, image is rotated by 90 °, 180 degree, 270 degree, data set is expanded 3 times.
Step 2, the design of convolutional neural networks
As shown in Fig. 2, convolutional neural networks model of the present invention is improved on the basis of existing VGG-16 models.
The mode input layer, hidden layer, output layer composition.But since the model in latter two layers of hidden layer is full articulamentum, and
The dimension of full articulamentum be 4096 dimensions, result in that the training parameter of the model is larger, and speed trained under equal conditions
Relatively slow, the convergence rate of model is slower.Therefore the present invention has abandoned full articulamentum, and full articulamentum is replaced with convolutional layer, because volume
Product operation also can effectively extract the feature of image.2048 will be tuned under latter two layers of characteristic dimension, so can preferably carried
The convergence rate of high model, and more preferable model can be obtained.And use average Chi Huadai in softmax layers of preceding layers
For maximum pond.
A. input layer
Input layer, by the data set obtained after expansion, data set is converted into by the medical image that step 1 proposes
Input of the lmdb forms as the model.
B. hidden layer,
Convolutional layer：First layer convolutional layer by the size of convolution kernel by 3*3 under be tuned into 1*1 so that convolution operation obtains
Characteristic pattern can more hold the detailed information of image.In the layer second from the bottom of image connection, and by spy entirely is replaced with full convolution
Sign dimension is reduced to the half connected entirely, substantially reduces the computation complexity of model, and improve the convergence rate of model.It is each
Layer port number is identical with input layer.
Pond layer：Using the convolution kernel of 2*2, it is applied in combination in first five layer and convolutional layer of the model.
C. output layer：
Output layer is connected with last full convolutional layer, and the dimension of output is equal with the classification number of medical image.The figure
The classification number of picture corresponds to the species number of disease.
Step 3, the training of convolutional neural networks
Under the caffe frames of Ubuntu systems, medical image is divided into training set, training set label, test set,
Test set label.Using back-propagation algorithm and stochastic gradient descent method, according to the size of the loss values of propagated forward, into
Row backpropagation iteration updates each layer of weight.When the loss values of model are intended to convergence, deconditioning model.In order to
Improve the convergence rate of model of the present invention, the method for introducing transfer learning, by the weight of trained VGG models in advance migrate to
On the model of the present invention, the weight of each layer of initial random assignment is avoided.Trained cycle, Yi Jineng can effectively be shortened
Access more preferable deep learning model.
Step 4, the feature extraction of medical image
Every piece image in data set is input in the deep learning model described in step 3.For the image of input,
The deep learning feature of 2048 dimensions is extracted in the full convolutional layer of the layer second from the bottom of image.Therefore a medical image sample
The vector of one 2048 dimension is can be obtained by, each sample in data set is corresponded into a row vector.And stick corresponding
Label, is built into the database of a deep learning feature.
Step 5, the identification of medical image.
For giving any one medical image to be identified, it is input in trained deep learning model, extracts sample
This deep learning feature.The present invention is trained, training for the first time is to pass through depth twice in order to improve the accuracy rate of identification
The method of study, extracts an effective deep learning feature；Second of training is by the depth having had been built up in step 4
Learning database, the training set by the use of traditional SVM methods using the data in database and label as SVM, further according to any
The deep learning feature that sample obtains, the test set as SVM.The medicine figure is effectively differentiated by method trained twice
As which classification belonged to.Experiment shows that this method can effectively improve the recognition accuracy of medical image.
Fig. 2 illustrates the method deep learning network structure using the present invention.The embodiment of the present invention is using improved
VGG-16 models.Comprise the following steps that：
1) manually medical images data sets are expanded, sets training set, training set label, test set, test set mark
Label；
2) by ready-made data set by under caffe environment, passing through the classification of the improved VGG-16 model specifications needs of Fig. 2
Num_oupt value, start training data, using back-propagation algorithm learning algorithm, and stochastic gradient descent method.According to
The size of the loss values of propagated forward, to carry out the weight that backpropagation iteration updates each layer.Until the loss values of model become
To in convergence when, deconditioning model.
3) batch input image, in the preceding layer of the softmax classification layers of model, 2048 dimensions of full convolutional layer extraction image
Characteristic of division, and as the training set of SVM.
4) for the training set of SVM obtained above, we set the label of training set, and our inputs of test set are then
It is the feature of the corresponding deep learning of test sample of test required for us.The accuracy rate that may finally be classified.
Those listed above is a series of to be described in detail only for feasibility embodiment of the invention specifically
Bright, they simultaneously are not used to limit the scope of the invention, all equivalent implementations made without departing from skill spirit of the present invention
Or change should all be included in the protection scope of the present invention.
Claims (9)
1. a kind of image-recognizing method based on deep learning, it is characterised in that include the following steps：
Step 1, gather and expand image data set：Limited image is pre-processed, the sample of image is expanded；
Step 2, convolutional neural networks are designed：The convolutional neural networks model is sharp on the basis of existing VGG-16 models
Replaced connecting layer building entirely with convolutional layer；
Step 3, training convolutional neural networks：Using back-propagation algorithm and stochastic gradient descent method, according to propagated forward
The size of loss values, to carry out the weight that backpropagation iteration updates each layer, when the loss values of model are intended to convergence,
Deconditioning model, obtains deep learning model；
Step 4, the feature of image is extracted：Every piece image in data set is input to the deep learning model described in step 3
In, for the image of input, in the full convolutional layer extraction deep learning feature of the layer second from the bottom of image；
Step 5, image is identified：For giving any one image to be identified, it is input in trained deep learning model,
The deep learning feature of sample is extracted, effectively differentiates which classification the image belongs to by method trained twice.
A kind of 2. image-recognizing method based on deep learning according to claim 1, it is characterised in that the step 1
The method of collection and the expansion of middle data set includes at least one of following methods：
Mirror image switch：Original image is subjected to left and right reversion, by 2 times of data extending；
Add salt-pepper noise：Some salt-pepper noises will be added in original image, by 2 times of data extending；
Segmentation figure：Target area in artwork is split, other regions are replaced with 0, by 2 times of data extending.
Add illumination：Image is rotated by 90 °, 180 degree, 270 degree, by 3 times of data extending.
A kind of 3. image-recognizing method based on deep learning according to claim 1, it is characterised in that the step 2
Convolutional neural networks model by input layer, hidden layer, output layer composition：
Data set, by the data set obtained after expansion, is converted into lmdb lattice by the input layer by the image that step 1 proposes
Input of the formula as the model；
The hidden layer includes convolutional layer and pond layer；Convolution kernel is sized to 1*1 in the first layer of the convolutional layer, is falling
The number second layer replaces full connection using full convolution, and characteristic dimension is reduced to the half connected entirely.The pond layer is using 2*2's
Convolution kernel, is applied in combination in first five layer and convolutional layer of the model；
The output layer is connected with last full convolutional layer, and the dimension of output is equal with the classification number of image to be identified.
4. a kind of image-recognizing method based on deep learning according to claim 1, it is characterised in that step 3 is trained
The method that transfer learning is used during convolutional neural networks, the weight of advance trained VGG models is migrated to described
On convolutional neural networks model.
A kind of 5. image-recognizing method based on deep learning according to claim 1, it is characterised in that the step 3
The training convolutional neural networks further include：Under the caffe frames of Ubuntu systems, view data is divided into training set,
Training set label, test set, test set label.
A kind of 6. image-recognizing method based on deep learning according to claim 1, it is characterised in that the step 4
Further include：Each sample in data set is corresponded into a row vector, and sticks corresponding label, is built into a depth
Practise the database of feature.
A kind of 7. image-recognizing method based on deep learning according to claim 1, it is characterised in that the depth
The dimension for practising feature is 2048 dimensions.
A kind of 8. image-recognizing method based on deep learning according to claim 1, it is characterised in that the step 5
Described in training twice be respectively：Training for the first time is the method by deep learning, extracts an effective deep learning
Feature；Second training is by the deep learning database having had been built up in step 4, using SVM methods by database
Training set as SVM of data and label, the deep learning feature obtained further according to arbitrary sample, the test as SVM
Collection.
9. according to a kind of image-recognizing method based on deep learning of claim 1-8 any one of them, it is characterised in that institute
State image-recognizing method and be applied to medical image recognition.
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