CN107680678A - Based on multiple dimensioned convolutional neural networks Thyroid ultrasound image tubercle auto-check system - Google Patents

Based on multiple dimensioned convolutional neural networks Thyroid ultrasound image tubercle auto-check system Download PDF

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CN107680678A
CN107680678A CN201710975555.1A CN201710975555A CN107680678A CN 107680678 A CN107680678 A CN 107680678A CN 201710975555 A CN201710975555 A CN 201710975555A CN 107680678 A CN107680678 A CN 107680678A
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tubercle
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CN107680678B (en
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李帅
宋文凤
刘吉
郝爱民
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Beihang University
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Abstract

The present invention provides one kind and is based on multiple dimensioned convolutional neural networks Thyroid ultrasound image tubercle auto-check system, including：Thyroid nodule is by thick tubercle sort module, thyroid nodule region automatic detection module and thyroid nodule essence sort module to essence；The feature of the size of different sensing regions is extracted by the convolutional neural networks of multiple dimensioned Fusion Features, thyroid nodule is automatically positioned so as to combine the context semantic feature of local and global information extraction tubercle.The present invention passes through the multiple dimensioned feature extraction by the thick neutral net to essence and the multiple dimensioned essence classification AlexNet by designing pyramid structure, the position of focus and the probability of good pernicious generation can accurately be predicted, doctor can be aided in carry out the diagnosis of thyroid gland focus, improve the objectivity of diagnosis, it is good with real-time, the characteristics of accuracy rate is high.

Description

Based on multiple dimensioned convolutional neural networks Thyroid ultrasound image tubercle auto-check system
Technical field
The present invention relates to based on multiple dimensioned convolutional neural networks Thyroid ultrasound image tubercle auto-check system, belong to deep Degree study and medical assistance field.
Background technology
In the 1940s, ultrasonic imaging technique is begun in clinical practice, in 1962, start to carry out thyroid gland Diagnosis.Thyroid ultrasound imaging technique can provide on thyroid hoc scenario for doctor, and it is first to have become modern doctor The diagnosis of technique means of choosing.
Convolutional neural networks technology plays the research history for having had more than 30 years so far, volume since last century the eighties Product neutral net is initially applied to the numeral identification of handwritten form, always is in identifications such as computer vision field, face, objects One very challenging study hotspot.In recent years, with internet development, the acquisition of mass data collection is no longer one Individual problem, 12 years, Alex obtained natural image Imagenet using convolutional neural networks with the advantage more than second place 20% The champion of match, hereafter convolutional neural networks started the new epoch, from object identification, recognition of face, object positioning, semanteme point The field such as cut and ranking list has all been refreshed with overwhelming advantage.
Observation analysis of the identification of thyroid excusing from death image before this always dependent on experienced doctor diagnoses, and not only consumes When effort, and because doctor's resource in China is unbalanced, it is difficult to accomplish the lifting of integral level.By means of convolutional neural networks For the advantage of natural image identification, there is high-quality medical resource to be converted into algorithm, new image is carried out the positioning of focus with Identification, is applied in the identification of Thyroid ultrasound.
In addition, the automatic diagnosis of Thyroid ultrasound to improve doctor operating efficiency play the role of it is important, have it is wide Application prospect and huge economic value.However, because excusing from death image has, contrast is low at present, the unconspicuous spy in border Sign, vision difference is small, and the research to thyroid ultrasonoscopy is also faced with lot of challenges, because these phenomenons contain more behind For the physics and visual signature mechanism of complexity, compellent simulated effect is realized, it is necessary to which multi-crossed disciplines are theoretical and soft or hard The highly effective algorithm that part combines is configured as supporting.
The content of the invention
Present invention solves the technical problem that it is：Overcome the deficiency of the diagnosis of existing Thyroid ultrasound image, there is provided one Kind is based on multiple dimensioned convolutional neural networks Thyroid ultrasound image tubercle auto-check system, realizes the automatic of thyroid nodule Positioning and identification, the positioning of tubercle and good pernicious judgement can be effectively carried out to Thyroid ultrasound image.
The technical solution adopted by the present invention is：Examined automatically based on multiple dimensioned convolutional neural networks Thyroid ultrasound image tubercle Disconnected system, it is characterised in that including：Thyroid nodule is by thick tubercle sort module, thyroid nodule region automatic detection to essence Module and thyroid nodule essence sort module；Wherein：
Thyroid nodule is by the thick tubercle sort module to essence：Including by the thick Thyroid ultrasound image tubercle to essence oneself Dynamic positioning and classification, described be automatically positioned include two stages with classification, and first stage is the rough sort stage, by number Data preprocess process, using the detection algorithm based on deep neural network VGG, extract first by by multiple dimensioned convolutional Neural institute The semantic feature of description, positioning and preliminary rough sort are carried out to possible thyroid nodule, and provide candidate frame；Second stage For the ultrasonoscopy block in candidate frame, nature is trained on ImageNet data sets using the Alex networks further lifted The parameter model of the disaggregated model of image is trained on the image data set of thyroid nodule and transfer learning；After training By model parameter extraction characteristic vector, the probable value of each class is obtained by softmax functions, to provide final tubercle Position and good pernicious judged result；
Thyroid nodule region automatic detection module：Including training and test phase, in the training stage, pass through basic network Multiple dimensioned convolution feature is extracted with the multiple dimensioned full convolutional network newly increased, the convolution kernel of multiple yardsticks is utilized in detection process The feature and context semantic information of multiple yardsticks of input picture are extracted, so as to obtain characteristic response figure, it is pre- to include tubercle The coordinate information of frame is surveyed, while carries out classification based training, it is good pernicious with predicted position and tentative prediction；Meanwhile design is based on grid Candidate region selection mechanism, to image block carry out feature extraction；Entered respectively by two object functions after image block extraction Row constraint is the softmax functions of classification respectively, for the position of tubercle, enters row constraint using the L2 norms of recurrence, simultaneously To nodule position information, the network parameter trained is preserved；In test phase, the image uploaded first to user is located in advance Reason, is then input in the parameter model trained；
Thyroid ultrasound image essence sort module：The position of the candidate frame obtained according to automatic detection module, to original Ultrasonoscopy intercept the image block of relevant position, i.e., the overall random interception of image is had the subgraph of fixed size, and handle Image block is input in smart Classification Neural, and the neural sorter network of essence classification extracts the property of multilayer feature using convolution operation Matter, with reference to the unfixed feature of Thyroid ultrasound image tubercle size, pyramid is introduced in the structure of smart Classification Neural Space structure, while multiple dimensioned pyramid convolution feature is extracted, pass through on the basis of multiple dimensioned pyramid convolution feature The good pernicious probability of softmax Function Estimations, provides the smart classification results for belonging to good Malignant Nodules.
The thyroid nodule ultrasonoscopy is as follows by the specific implementation process of the thick tubercle sort module to essence：
(1) data prediction is carried out to the Thyroid ultrasound image of acquired original by data preprocessing method and data increases By force, pretreatment includes carrying out data size replacement, and data strengthen to ultrasonoscopy Mirroring Mapping, random piecemeal, overall diagram As being divided into different image-regions, to increase the amount of training data；
(2) by by thick to two stages of essence, in the rough sort stage, for detect tubercle by multiple dimensioned convolutional Neural The semantic feature described by end-to-end automatic detection network model on VGG network foundations, while by detecting thyroid nodule Judge that its is good pernicious while region, obtain the candidate frame of thyroid nodule and the result of rough sort to possible thyroid nodule Carry out positioning and preliminary rough sort；
(3) in smart sorting phase, after data prediction and data enhancing are carried out to original labeled data, according to candidate Ultrasonoscopy block in frame, the figure in candidate frame is further classified by the AlexNet neutral nets of redesign The good pernicious judged result determined, utilizes the Alex networks further lifted, it is proposed that for thyroid ultrasonic tubercle Multiple dimensioned pyramid convolutional neural networks, and on ImageNet data sets train natural image disaggregated model parameter Model is trained on the image data set of thyroid nodule.By model parameter extraction characteristic vector after training, pass through Softmax functions obtain the probable value of each class, to provide the position of final tubercle and good pernicious judged result.
Thyroid nodule region automatic detection module is implemented as follows：
In the training stage,
(1) the multiple dimensioned convolution kernel feature of extraction logical first, specifically utilizes net based on VGG-16 convolutional neural networks Network feature extraction, the full convolutional layer of three yardsticks is then added, the wherein size of convolution kernel is 1,3,5,7 further to extract The feature of different scale and the context semantic information in space, so as to obtain characteristic response figure, include the seat of tubercle prediction block Information is marked, while carries out classification based training, it is good pernicious with predicted position and tentative prediction；
(2) the candidate region selection mechanism based on grid is designed, after carrying out feature extraction based on general image, is being rolled up entirely Being designed after lamination increases multiple fixed proportion sized images blocks, and same image averaging is divided into difference according to tile size The small image block of ratio, and carry out feature extraction further directed to the image block in frame；
(3) the softmax functions that row constraint is classification respectively are entered by two object functions respectively after image block extraction, Input is the Feature maps that probe frame passes through after convolutional layer, and output is into good pernicious probability；Meanwhile for tubercle Position, enter row constraint using the L2 norms of recurrence, while obtain nodule position information, preserve the network parameter trained；
(4) pre-processed in test phase, the image uploaded first to user, be then input to the parameter mould trained In type.
The Thyroid ultrasound image essence sort module is implemented as follows：
(1) data prediction and data are carried out to original labeled data first strengthens, wherein, data prediction includes pair Data carry out size normalization, and the normalization of distribution, its distribution is belonged to normal distribution, average 0, variance 1；
(2) and then by the Alexnet of first five layer the feature that feature extraction tentatively extracts Thyroid ultrasound image is carried out；
(3) in layer 6, it is proposed that for the multiple dimensioned pyramid convolutional neural networks of thyroid ultrasonic tubercle, After layer 5 input, all passages are split into three passages, extract the feature of the size of different convolution kernels side by side, point The spatial pyramid structure of prestige prestige 1,3,5,7, finally merges into same passage；
(4) after merging, row constraint is entered by using the object function softmax of forecast classification, and in ImageNet numbers It is trained according to the parameter model for the disaggregated model that natural image is trained on collection on the image data set of thyroid nodule.Simultaneously Gradient decline is carried out using back-propagation algorithm, every a kind of probable value is obtained, to provide the position of final tubercle and good evil The judged result of property.
The principle of the present invention is：
(1) by carrying out automatic detection and classification to smart multiple dimensioned mode by thick, calculated using based on SSD method The candidate frame of thyroid tubercle.In order to take into account thyroid multiple dimensioned and polymorphic feature.
(2) present invention devises the multi-level network structure for judging network structure completion rough sort and essence classification.
(3) in order to carry out the automatic detection of thyroid nodule, the present invention is by based on SSD automatic detection networks, it is proposed that A kind of automatic detection convolutional neural networks mapped by multiple features.This method is calculated by using convolutional neural networks Feature Mapping, whole mapping graph is carried out to the decomposition of multiple yardsticks, the image block after decomposition classified, to predict first shape The position of gland tubercle, and the tubercle in image block is automatically classified.
(4) in order to further be classified to thyroid tubercle, the present invention passes through the pyramidal feature in design space UNE structure is diagnosed automatically to the thyroid nodule of automatic detection in (2).
The present invention being a little compared with prior art：
(1) it is proposed by the present invention based on by thick automatic detection and identification method to essence, it on the one hand can carry out quick standard True detection.On the other hand the result for providing and accurately identifying can be layered.And the feature of multiple dimensioned spatial pyramid is carried out UNE structure.
(2) existing target automatic detection algorithm is contrasted, it is proposed by the present invention to be based on the multiple dimensioned polymorphic spy of thyroid gland Sign mapping blending algorithm, can adapt to the polymorphic automatic detection and rough sort of thyroid nodule.
(3) it is proposed by the present invention based on the pyramidal automatic classification of thyroid nodule multiscale space, can be to thyroid gland Different scale and the tubercle of form classify automatically.
Brief description of the drawings
Fig. 1 is being formed based on multiple dimensioned convolutional neural networks Thyroid ultrasound image tubercle auto-check system for the present invention Block diagram；
Fig. 2 is based on multiple dimensioned convolutional neural networks Thyroid ultrasound image tubercle auto-check system flow for the present invention Figure；
Fig. 3 is by the thick tubercle sort module implementation process figure to essence；
Fig. 4 is thyroid nodule region automatic detection module implementation process figure；
Fig. 5 is thyroidine sort module implementation process figure；
Fig. 6 is the output result of automatic recognition system, thyroid nodule detection and classification results schematic diagram.
Embodiment
The present invention is further illustrated with reference to other accompanying drawings and embodiment.
As shown in Figure 1, 2, it is of the invention to be diagnosed automatically based on multiple dimensioned convolutional neural networks Thyroid ultrasound image tubercle System is included by thick tubercle sort module, thyroid nodule region automatic detection module and thyroidine sort module to essence.
Fig. 1 is based on multiple dimensioned convolutional neural networks Thyroid ultrasound image tubercle auto-check system, including three modules： Thyroid nodule region automatic detection module, thyroid nodule by slightly to smart sort module and Thyroid ultrasound essence sort module, Wherein automatic detection module key technology is multiple dimensioned convolutional layer fusion, and the key technology of second module is full images candidate knot Save the function constraint that position returns and classified；3rd module is the design of Analysis On Multi-scale Features pyramid structure.
Fig. 2 is to be based on multiple dimensioned convolutional neural networks Thyroid ultrasound image tubercle auto-check system flow chart, first Carry out knuckle areas automatic detection, then by by the thick taxonomy model progress tubercle to essence be automatically positioned and rough sort, most Rough sort result carries out precise classification afterwards.
As shown in figure 3, realized by the thick thyroid nodule sort module to essence as follows：
(1) data prediction and data enhancing are carried out to the Thyroid ultrasound image of acquired original first.Pretreatment includes The size that size is reset to 300*300 fixation is carried out to image.Data strengthen to ultrasonoscopy Mirroring Mapping, random piecemeal, General image is divided into different image-regions；
(2) module includes, in the rough sort stage, inputting ultrasonoscopy to be sorted, more chis to smart two stages by thick It is respectively that 3,5,7 grade convolution kernels (size is consistent with VGG-16) are rolled up to spend convolutional Neural VGG networks to carry out 16 convolution sizes Product, the semantic feature described by end-to-end automatic detection network model, its Feature maps is calculated by convolution and pond layer, Three thyroid gland convolutional layers are added on the basis of basic network, for the layer newly added, pass through 32,64,128 volumes respectively Product core is predicted.For the characteristic layer that a size is w*h, c passage, it is predicted using 5*5 convolution kernel, is entirely being schemed The probability bivector of the position of tubercle is predicted on picture, the element of the bivector is good pernicious score respectively.Finally, it is defeated Go out the result of the rough sort of tubercle；
(3) in smart assorting process, the ultrasonoscopy block region in candidate frame, the AlexNet redesigned is configured Neutral net carries out the good pernicious judged result that is determined of further classify to the image in candidate frame, utilizes and is further lifted Alex networks trained on ImageNet data sets natural image disaggregated model parameter model thyroid nodule figure As being trained on data set and transfer learning.By model parameter extraction characteristic vector after training, pass through softmax functions Every a kind of probable value is obtained, to provide the position of final tubercle and good pernicious judged result.
As shown in figure 4, automatic detection module realization in thyroid nodule region is as follows：
(1) the Thyroid ultrasound image, it is necessary to global is extracted for the unconspicuous thyroid feature of ultrasound in border first Context semantic information auxiliary judgment.Relatively large convolution yardstick is devised herein and carries out amalgamation of global information, is used first VGGNet carries out tuning and extraction Feature map, and the segmentation of image block is carried out on Feature map, and uses fixed size The candidate frame of size be scanned, be predicted.The size of candidate frame now applies to thyroid tubercle.Tubercle Ratio typically 4:1 and 1：Between 5.The yardstick of candidate frame is also in the range of this to adapt to the detection of thyroid tubercle.
(2) and then after last pooling layer of network above-mentioned candidate frame is added.Acted in the form of convolution To feature map.On each image block, the classification that the region belongs to is predicted by network structure.Each image block There is the prediction result of the candidate frame of the size of 10 ratios.The characteristics of image of multiple yardsticks can be thus predicted.
(3) in Feature maps L nodule candidate frame of each position prediction.For each candidate frame, 2 classes are predicted Other score, and 4 deviants relative to candidate frame set in advance, (2+4) * L fallout predictors are so needed, w*h's (2+4) * L*w*h predicted values will be produced on Feature maps.
(4) finally, the ratio per a kind of sample of loss function is controlled to carry out.The ratio taken is 3:1, pernicious knot The weight of section account for 75%.To lift the importance of classification.It is capable of the classification pernicious sample of more accuracy rate.
Lcla=0.75lp+0。25ln
Lall=Lloc+Lcla
Wherein, LclaThe loss function of presentation class, the present invention is using Softmaxloss.LlocIt is the recurrence of position Loss function, take is L2 norms to the present invention.The backpropagation for calculating L2 norms carries out the renewal of model parameter.By with The constraint of upper two formula obtains the rough sort result of thyroid nodule and the position of tubercle.
As shown in figure 5, smart Classification Neural module realization is as follows：
(1) scale size of the image block in the candidate frame obtained in automatic detection module is reset to 224*224 first, Calculate image and the convolution kernel of initialization carries out sliding window scanning, pond is carried out on obtained new Feature maps pictures Operation, half of the image down sampling to original size；
(2) Feature maps activation primitive layer ReLu is calculated, is controlled in the range of [0, just infinite], is calculated The result of corresponding multiplication between Feature maps and Relu activation primitive elements, the small feature extraction of filter value mainly influence Semantic category another characteristic；
(3) divide by 6 layers of convolution and pond and ReLu operations, layer 6s of the Feature maps in AlexNet Convolution kernel for three passages is parallel, and convolution kernel size is respectively 3,5,7, calculates Feature maps with after convolution, obtaining New Feature maps, then carry out multi-channel feature merging, finally by the training of full articulamentum obtain characteristic vector with Carry out category classification；
(4) gradient is calculated by back-propagation algorithm and stochastic gradient descent method, carries out parameter renewal；
(5) by the model parameter obtained in (4), feature extraction is carried out to test image, then passes through softmax functions Carry out good pernicious prediction, obtain Two-dimensional Probabilistic vector, to it is good it is pernicious judge, export good pernicious result.
As shown in fig. 6, thyroid nodule automatic detection and the displaying of rough sort module results.
Thyroid nodule automatic detection result is the position of tubercle and the output of candidate frame, wherein dark and light color frame For output result, dark color is pernicious, and light color is benign.Present system position is accurate, and can navigate to doctor and mark manually The tubercle of the spill tag of note.
Shown in the following form of accuracy rate
Title Accuracy rate (variance)
AlexNet 94.7% (1.1)
GoogleNet 95.2% (2.2)
AlexNet+ pyramid structures 94.5% (3)
GoogleNet+ pyramid structures 96.2% (2)
The present invention 97.4% (2)
Above table shows the of the invention and algorithm of existing main flow comparative result, and first row is the name of algorithm, the Two row are corresponding results, and variance is represented inside bracket.Accuracy rate is the average tested three times.In order to verify the present invention in data Stability and generalization on collection, carried out 3 experiments, data set have 4000 samples, 2000 positive samples and 2000 Negative sample, randomly choose and be trained on 80% training set, 20% is tested, and the accuracy rate for showing the present invention is 97.4%, it is above other systems.

Claims (4)

1. it is based on multiple dimensioned convolutional neural networks Thyroid ultrasound image tubercle auto-check system, it is characterised in that：Including first Shape gland tubercle is by thick to the tubercle sort module of essence, thyroid nodule region automatic detection module and thyroid nodule essence classification mould Block；Wherein：
Thyroid nodule is by the thick tubercle sort module to essence：Including by the automatic fixed of the thick Thyroid ultrasound image tubercle to essence Position and classification, described be automatically positioned include two stages with classification, and first stage is the rough sort stage, pre- by data Processing procedure, using the detection algorithm based on deep neural network VGG, extract first by as described by multiple dimensioned convolutional Neural Semantic feature, positioning and preliminary rough sort are carried out to possible thyroid nodule, and provide candidate frame；Second stage is root According to the ultrasonoscopy block in candidate frame, natural image is trained on ImageNet data sets using the Alex networks further lifted The parameter model of disaggregated model be trained on the image data set of thyroid nodule and transfer learning；Pass through after training Model parameter extraction characteristic vector, the probable value of each class is obtained by softmax functions, to provide the position of final tubercle With good pernicious judged result；
Thyroid nodule region automatic detection module：Including training and test phase, in the training stage, by basic network and newly Increased multiple dimensioned full convolutional network extracts multiple dimensioned convolution feature, is extracted in detection process using the convolution kernel of multiple yardsticks The feature and context semantic information of multiple yardsticks of input picture, so as to obtain characteristic response figure, include tubercle prediction block Coordinate information, while carry out classification based training, it is good pernicious with predicted position and tentative prediction；Meanwhile design the time based on grid Favored area selection mechanism, feature extraction is carried out to image block；Carried out about by two object functions respectively after image block extraction Beam is the softmax functions of classification respectively, for the position of tubercle, enters row constraint using the L2 norms of recurrence, while tied Positional information is saved, preserves the network parameter trained；In test phase, the image uploaded first to user pre-processes, so It is input to afterwards in the parameter model trained；
Thyroid ultrasound image essence sort module：The position of the candidate frame obtained according to automatic detection module, to original ultrasound Image intercept the image block of relevant position, i.e., overall random subgraph of the interception with fixed size of image, and image Block is input in smart Classification Neural, and the neural sorter network of essence classification utilizes the property of convolution operation extraction multilayer feature, ginseng The unfixed feature of Thyroid ultrasound image tubercle size is examined, pyramid space knot is introduced in the structure of smart Classification Neural Structure, while multiple dimensioned pyramid convolution feature is extracted, pass through softmax functions on the basis of multiple dimensioned pyramid convolution feature Estimate good pernicious probability, provide the smart classification results for belonging to good Malignant Nodules.
2. according to claim 1 diagnosed automatically based on multiple dimensioned convolutional neural networks Thyroid ultrasound image tubercle is System, it is characterised in that：The thyroid nodule ultrasonoscopy is as follows by the specific implementation process of the thick tubercle sort module to essence：
(1) carrying out data prediction and data to the Thyroid ultrasound image of acquired original by data preprocessing method strengthens, in advance Processing includes carrying out data size replacement, and data strengthen to ultrasonoscopy Mirroring Mapping, random piecemeal, general image split For different image-regions, to increase the amount of training data；
(2) by by thick to two stages of essence, in the rough sort stage, for detect tubercle by multiple dimensioned convolutional Neural VGG nets The semantic feature described by end-to-end automatic detection network model on the basis of network, while by detecting thyroid nodule region Judge that its is good pernicious simultaneously, obtain the candidate frame of thyroid nodule and the result of rough sort is determined possible thyroid nodule Position and preliminary rough sort；
(3) in smart sorting phase, after data prediction and data enhancing are carried out to original labeled data, according in candidate frame Ultrasonoscopy block, further classification is carried out to the figure in candidate frame by the AlexNet neutral nets of redesign and is obtained The good pernicious judged result determined, utilizes the Alex networks further lifted, it is proposed that for the more of thyroid ultrasonic tubercle The pyramid convolutional neural networks of yardstick, and on ImageNet data sets train natural image disaggregated model parameter model It is trained on the image data set of thyroid nodule；By model parameter extraction characteristic vector after training, pass through Softmax functions obtain the probable value of each class, to provide the position of final tubercle and good pernicious judged result.
3. according to claim 1 diagnosed automatically based on multiple dimensioned convolutional neural networks Thyroid ultrasound image tubercle is System, it is characterised in that：Thyroid nodule region automatic detection module is implemented as follows：
In the training stage,
(1) first by extracting multiple dimensioned convolution kernel feature, network based on VGG-16 convolutional neural networks is specifically utilized Feature extraction, the full convolutional layer of three yardsticks is then added, the wherein size of convolution kernel is 3,5,7 further to extract difference The feature of yardstick and the context semantic information in space, so as to obtain characteristic response figure, include the coordinate letter of tubercle prediction block Breath, while classification based training is carried out, it is good pernicious with predicted position and tentative prediction；
(2) the candidate region selection mechanism based on grid is designed, after carrying out feature extraction based on general image, in full convolutional layer Design increases multiple fixed proportion sized images blocks afterwards, and same image averaging is divided into different proportion according to tile size Small image block, and further directed in frame image block carry out feature extraction；
(3) the softmax functions that row constraint is classification respectively, input are entered by two object functions respectively after image block extraction It is the Feature maps that probe frame passes through after convolutional layer, output is into good pernicious probability；Meanwhile for the position of tubercle Put, enter row constraint using the L2 norms of recurrence, while obtain nodule position information, preserve the network parameter trained；
(4) pre-processed in test phase, the image uploaded first to user, be then input to the parameter model trained In.
4. according to claim 1 diagnosed automatically based on multiple dimensioned convolutional neural networks Thyroid ultrasound image tubercle is System, it is characterised in that：The Thyroid ultrasound image essence sort module is implemented as follows：
(1) data prediction and data are carried out to original labeled data first strengthens, wherein, data prediction is included to data Size normalization, and the normalization of distribution are carried out, its distribution is belonged to normal distribution, average 0, variance 1；
(2) and then by the Alexnet of first five layer the feature that feature extraction tentatively extracts Thyroid ultrasound image is carried out；
(3) in layer 6, it is proposed that for the multiple dimensioned pyramid convolutional neural networks of thyroid ultrasonic tubercle, the 5th After layer inputs, all passages are split into three passages, the feature of the size of different convolution kernels is extracted side by side, is divided into 1, 3,5,7 spatial pyramid structures, finally merge into same passage；
(4) after merging, row constraint is entered by using the object function softmax of forecast classification, and in ImageNet data sets The parameter model of the disaggregated model of upper training natural image is trained on the image data set of thyroid nodule, is utilized simultaneously Back-propagation algorithm carries out gradient decline, obtains every a kind of probable value, to provide the position of final tubercle and good pernicious Judged result.
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