284results about How to "Accurate segmentation" patented technology

The invention provides an auricle anaphora segmentation method and system, and the method comprises the steps: inputting a to-be-segmented human ear image and text description into an auricle anaphora segmentation model which comprises a text coding module, a text-guided visual coding module and visual decoding module, and an angle transformation module; the text coding module obtains a text feature # imgabs0 #; a visual coding module realizes fusion of text features and image features through a structure organized into four stages, a visual encoder in each stage generates visual features # imgabs1 #, a cross-modal sensing module aligns # imgabs2 # and # imgabs3 # to obtain multi-modal features # imgabs4 # and # imgabs5 #, each element in the multi-modal features # imgabs4 # and # imgabs5 # is weighted by an attention gating module to obtain weighted multi-modal features # imgabs6 #, and the weighted multi-modal features # imgabs6 # and # imgabs5 # are weighted by an attention gating module. Adding the elements and # imgabs7 # to generate an enhanced visual feature # imgabs8 #, inputting # imgabs9 # into a visual decoding module to gradually recover the spatial resolution of the image, and further fusing the text and the visual feature to output a multi-scale feature; the angle transformation module carries out angle transformation on the multi-scale features and outputs a segmentation mask of a text description related region. According to the invention, the auricle can be subjected to anaphora segmentation.

The invention relates to the technical field of medical treatment, in particular to an AI-based medical image automatic segmentation and labeling system, which comprises an image data acquisition module, a data preprocessing module, a feature extraction module, an automatic segmentation module and an intelligent labeling module, the data preprocessing module improves image quality through noise suppression, artifact removal and modal alignment, the feature extraction module extracts a feature map of a potential lesion area, and the automatic segmentation module combines a full convolutional network, a bidirectional long-short-term memory network and an attention mechanism to realize accurate segmentation of an organ area and the lesion area. The intelligent labeling module is used for labeling anatomical names, lesion types and pathological features of the segmentation results by using a medical knowledge graph and a semantic embedding technology; the method has the advantages of being high in segmentation precision, comprehensive in marking and high in expansibility, and the automation and intelligence level of medical image analysis can be improved.

The invention provides an image segmentation method and device combining feature difference recognition and detail enhancement, and the method comprises the following steps: obtaining a to-be-segmented image to construct a data set, and carrying out the marking and preprocessing of the data set; constructing an image segmentation model, wherein the image segmentation model comprises an encoder network, a jump connection network, a decoder network and an output network; the encoder network uses a pre-trained CNN (Convolutional Neural Network) and Transform hybrid network to carry out feature extraction; the jump connection network comprises a channel space interaction module for fusing encoder features and decoder features; the decoder network comprises a convolution-channel attention module for extracting key features and a convolution-up-sampling module for up-sampling; the output network comprises a multi-level feature fusion module for fusing the multi-scale features of the decoder; training the image segmentation model to obtain a trained image segmentation model; and segmenting the image data by adopting the trained image segmentation model.

The invention belongs to the technical field of image recognition, and discloses a few-sample defect detection method based on a prototype prompt fine-tuning visual basic model SAM. Prototype prior information about an image to be segmented is extracted from a small number of example image-mask pairs, so that prior guidance of human beings is replaced; for defect data, a representative foreground prompt point, a high-confidence background prompt point (sparse prompt) and a foreground Mask (dense prompt) are extracted from prototype prior to serve as prototype prompt, and a visual basic model SAM is guided to achieve more accurate segmentation. A visual basic model SAM is finely adjusted by using prototype prompt and a small amount of defect data, a prompt encoder and an image encoder of the SAM are frozen, an LoRA bypass is injected into the image encoder, and the SAM is enabled to better adapt to defect data distribution by updating parameters of the LoRA bypass and a lightweight mask decoder.

The invention relates to a polyp image segmentation method based on pyramid vision Transform, and belongs to the technical field of digital medical treatment. The method comprises the following steps: capturing global context information through a pyramid vision Transform module, and obtaining feature maps with different resolutions from a plurality of extraction stages; detail information is extracted from the shallow feature map of the first stage through a cyclic cross attention module, and shallow features are obtained; a multi-scale feature fusion sensing module is used for carrying out fusion processing on the feature maps extracted in multiple stages, deep features are extracted, and the deep features and shallow features are fused and then used for image segmentation; and designing weighted binary cross entropy loss and weighted IoU loss to train the above process, and segmenting the polyp image after training is completed. According to the invention, through the pyramid vision Transform module, the cyclic cross attention module and the multi-scale feature fusion perception module, the global information learning ability, the detail information capturing ability and the segmentation performance are significantly improved.

The invention belongs to the technical field of computer vision, provides a real-time semantic perception system running on equipment, performs real-time instance segmentation and semantic mapping on a working environment, and provides a perception method based on lightweight real-time instance segmentation and semantic mapping. In the first part, firstly, a color image containing color and texture information is used for object classification and bounding box regression; cutting the original depth image into a depth image with an accurate geometric edge, and inputting the depth image into the truncation network to generate a mask of the instance; a second part: (1) calculating the attitude and size of a target object in the view of the camera through principal component analysis; (2) obtaining the category of the object in the view obtained by segmentation; (3) creating a point cloud of the depth image projection of the target object in a map; and then cross-frame tracking and fusion are carried out by using data association to realize the purpose of sensing. The method is mainly applied to automatic sensing occasions.

The invention discloses a mobile phone screen glass defect detection method based on improved YOLOv8, and relates to the technical field of mobile phone screen glass cover plate quality detection. The method comprises the following steps: simulating an industrial production environment, collecting mobile phone screen glass images, and constructing a data set; improvement and optimization are carried out on the basis of the YOLOv8 target detection network model, a mobile phone screen glass defect detection classification model is constructed, and parameters are trained and optimized; constructing a semantic segmentation model of the YOLOv8 network, segmenting the defects of the mobile phone screen glass image to be identified, and realizing detection and identification of different defect category edges; and fitting the actual damage size of the mobile phone screen glass defect range. The mobile phone screen glass defect detection system is suitable for accurate detection of mobile phone screen glass defect characteristics, can measure the defect range, and provides intelligent and efficient services for the mobile phone screen glass quality inspection process.

The invention relates to the technical field of image processing, in particular to a laser welding quality evaluation method, which comprises the following steps of: acquiring a grey-scale map of a welding image, dynamically adjusting the size of an initial region of each pixel point to determine a local region of each pixel point, forming a feature descriptor of each pixel point by using feature values of multiple dimensions of the local region, and determining the welding quality of the welding image according to the feature descriptor. Calculating the similarity of all the pixel points based on the feature descriptors of all the pixel points, performing clustering based on the similarity, and calculating the weight of each category after clustering; and according to the weight of each category and the average feature value of the feature descriptors of the pixel points contained in the category in each dimension, adjusting the initial adaptive segmentation threshold of each pixel point, performing adaptive threshold segmentation, and evaluating the welding quality based on the result of the adaptive threshold segmentation. And the accuracy of adaptive threshold segmentation and the accuracy of a welding quality evaluation result are improved.

The invention discloses a water body remote sensing recognition method based on hyperspectral data simulation and U-net. The water body remote sensing recognition method comprises the following steps: A, data collection and preprocessing; b, selecting and manufacturing a sample area; c, data simulation; d, derived parameters are calculated; e, manufacturing a training sample and a verification sample; f, training a U-net network model; g, multispectral water body detection; and H, precision evaluation. According to the method, a high-quality training sample is obtained through a hyperspectral remote sensing image, multispectral data of different sensors are simulated, the problem that the reusability of water body classification samples among different multispectral satellites and at different geographic distances and time is not sufficient is solved, the multispectral data Landsat8 and Sentinel2 are taken as examples, and the reusability of the water body classification samples is improved. It is proved that by means of the method, easily-confused ground objects such as building shadows and cloud shadows in the water body recognition process can be effectively removed, and on the basis of accurately extracting large-area rivers and lakes, the recognition precision of small lakes and ponds is improved.

The invention discloses a microcirculation blood vessel automatic segmentation and quantification method based on enhanced feature fusion. The method comprises the following steps: step 1, data preparation and preprocessing; 2, designing an enhanced feature fusion network model; step 3, automatically segmenting microcirculation blood vessels; and 4, quantifying the diameter of the blood vessel based on the segmentation result. According to the enhanced feature fusion network model provided by the invention, accurate segmentation of microvessels is realized through fusion and enhancement of multi-scale features. The model shows good generalization ability on diversified clinical image data through effective data enhancement, and can adapt to image data with different illumination conditions, contrast ratios and resolutions. Therefore, the model has high practicability and reliability in practical application. According to the method, the diameter of the blood vessel is quantified through the segmentation result, the diameter of the microvessel can be accurately measured, and powerful support is provided for evaluation of the clinical microcirculation function.

The invention discloses a polyp segmentation method based on edge guidance and deep supervision PVT. The method aims at a colonoscope image with a polyp, and outputs the position and the shape of the polyp by using a constructed polyp segmentation network. The polyp segmentation network uses PVT-v2 as a backbone network for feature extraction, and further comprises a context fusion decoding branch, a boundary extraction branch, a boundary sensing module and a cascade feature fusion branch. And the context fusion decoding branch serves as an image decoder and receives a semantic feature map output by the PVT-v2 backbone network. And the boundary extraction branch enriches the content of the detail feature map by using a refining module to obtain an edge prediction result. And the boundary sensing module fuses the refined features and the output feature map of the context fusion decoding to obtain a boundary sensing prediction result. And the cascaded feature fusion branch fuses fusion features under different scales layer by layer according to a sequence from deep to shallow, and outputs a final polyp segmentation prediction result P5.

The invention discloses a remote sensing image ship segmentation method based on a U-KAN network model. The method comprises the following steps: constructing the U-KAN network model; the U-KAN network model is a hybrid network architecture fusing a U-Net network and a KAN network; wherein the U-Net network comprises an encoder and a decoder which are in jump connection; a plurality of KAN layers are nested in the KAN network; u-KAN network model training is carried out in combination with the remote sensing image data set, and an optimized U-KAN network model is obtained; and inputting a remote sensing image to be processed into the optimized U-KAN network model to obtain a corresponding ship segmentation result. The method not only can effectively capture ship targets with different scales and complexities in the remote sensing image, but also can realize effective fusion between shallow detail features and deep semantic features, thereby overcoming the limitation in processing complex backgrounds and diversified ship forms in the prior art.

The invention relates to the technical field of intelligent analysis of aluminum oxide, in particular to an intelligent data analysis system and method based on aluminum oxide production. Acquiring ball mill parameters, raw material parameters and a spherical alumina impurity threshold, and determining a standard threshold according to the ball mill parameters and the raw material parameters; sampling and detecting the processed spherical aluminum oxide to obtain a purity and morphology image set of the spherical aluminum oxide; constructing a morphology image segmentation model to segment the morphology image set to obtain a first image set and a second image set; constructing a first image recognition model to recognize the first image set to obtain a spherical alumina parameter set, and calculating to obtain a first coefficient; constructing a second image recognition model to recognize the second image set to obtain an impurity parameter set, and calculating to obtain a second coefficient; and calculating the quality coefficient according to the first coefficient, the second coefficient, the spherical alumina impurity threshold and the spherical alumina purity. The comprehensive evaluation of the spherical aluminum oxide is realized through the quality coefficient of the spherical aluminum oxide.

The invention belongs to the technical field of machine vision and deep learning, and discloses an engineering structure crack detection and measurement method based on machine vision, and the method comprises the steps: collecting original image data of an engineering structure, discretizing the original image data into image blocks, carrying out the preprocessing, building a crack classification model through a Resnet-50 neural network, and carrying out the training, the method comprises the following steps of: firstly, constructing an image block, identifying a crack image in the image block, then constructing a SegFormer segmentation model based on a Transformer framework to segment the crack image to obtain a crack mask graph, evaluating the performance of the two models, then analyzing the two models by adopting an orthogonal skeleton line method, obtaining a contour line and a skeleton line of a crack, and calculating the length of the crack. According to the method, the ResNet-50 image classification model and the SegFormer segmentation model are combined to realize high-precision crack identification and segmentation of the building structure crack, the contour line and the skeleton line of the crack are obtained and the length of the crack is measured by means of the orthogonal skeleton line method, and the crack of an engineering structure is well detected and subjected to risk assessment.

The invention discloses an intelligent segmentation method and system for space steel structure nodes, and belongs to the technical field of building construction. The method comprises the following steps: collecting point cloud data of a steel structure, and preprocessing the point cloud data; clustering the point clouds to form a plurality of super voxels, and calculating concavity and convexity between adjacent super voxels; abstracting boundaries between adjacent super voxels into point clouds with convex-concave information, endowing the concave points and the convex points with weight values, and calculating an optimal segmentation plane; according to the method, the super voxels are segmented according to the optimal segmentation plane, and the segmentation result is optimized, so that the nodes are accurately segmented from the whole steel structure model, and the nodes are conveniently monitored by using a finite element model. The method can achieve the intelligent and rapid recognition and segmentation of the complex space steel structure nodes, remarkably improves the recognition and segmentation efficiency of the nodes, can guarantee the high consistency of segmentation results, improves the precision of the segmentation results, and can guarantee the comprehensiveness and accuracy of damage detection.

The invention discloses a subsea tunnel pavement crack extraction method and system, and relates to the technical field of image recognition processing, and the method comprises the steps: obtaining a to-be-segmented real crack image; inputting a real crack image to be segmented into the CycleGAN network pre-training model to obtain a migration image; the CycleGAN network pre-training model is a trained model obtained by training based on a virtual crack image data set and a real crack image data set, and comprises two pairs of generators and discriminators for alternate adversarial training. Obtaining a migration image similar to crack characteristics in the virtual crack image; and inputting the migration image into the semantic segmentation pre-training model, and extracting to obtain a corresponding crack region in the real crack image to be segmented. According to the invention, the inter-domain difference between the virtual crack image data set and the real crack image data set can be reduced, and the segmentation precision of the model on the real crack image is improved.

The invention provides an AIGC video creation method for cultural relic explanation, and belongs to the technical field of AIGC video creation. S2, cultural relic video processing; s3, audio data processing; and S4, carrying out fusion synthesis treatment. According to the method, the high-precision video processing effect and the audio authenticity processing effect can be increased, meanwhile, the interestingness can be increased by adding 3D personnel, and cultural relics can be known from multiple angles through cultural relic 3D restoration.

The invention relates to the technical field of rice quality detection, in particular to a head rice rate online detection method and system based on machine vision, and the method comprises the steps: obtaining an initial binary image of a rice image; intelligently screening a rice grain adhesion region in the initial binarized image by using a BP neural network to obtain a to-be-segmented binarized image; segmenting each rice grain adhesion region in the to-be-segmented binary image by using a multi-distance transformation watershed segmentation method to obtain a first segmentation result image; segmenting a rice grain adhesion area in the first segmentation result image by using an angle limitation concave point matching segmentation method to obtain a second segmentation result image as a final segmentation result image; and obtaining the length of each rice grain in the rice image on the basis of the obtained segmentation result image by using a minimum bounding rectangle method so as to calculate the head rice rate of the rice image. According to the method, the segmentation accuracy is improved, the segmentation method is high in applicability, and subsequent measurement of characteristic parameters of single-grain rice is facilitated.

The invention discloses a multi-attention mechanism network model and a thyroid ultrasound contrast focus segmentation method and system based on a multi-attention mechanism network. The multi-attention mechanism network model adopts an RFM-ResNeXt module to extract spatial and temporal feature information of an input video; aggregating the three-dimensional feature maps into a two-dimensional feature map through a bidirectional convolution LSTM module; inputting the two-dimensional feature map into a double attention module (DAM) to obtain an attention feature map; and carrying out convolution and up-sampling on the attention feature map to obtain a binary segmentation result image. The time-space information of the ultrasound contrast video can be effectively extracted by utilizing three attention mechanisms of channel attention, time attention and space attention, and the ablation area in the thyroid ultrasound contrast image can be automatically and accurately segmented.

The invention provides a feature weight adaptive super-pixel segmentation method based on a sonar image. The method comprises the following steps: Step 1, converting an original sonar image into a grayscale image; step 2, denoising processing is carried out on the image by using guide filtering; step 3, extracting specific features of the sonar image; step 4, carrying out superpixel segmentation preprocessing of automatic adjustment of feature weights; step 5, carrying out maximum flow-minimum cut superpixel aggregation; step 6, carrying out aggregation on the super pixels through a preset threshold value; and Step 7, separating the target from the background to obtain a segmentation result. According to the method, a segmentation technology suitable for the target object area of the acoustic image can be realized, and a better image foundation is laid for subsequent target object disease type judgment and volume and area measurement through analysis of the segmented area, so that segmentation is more accurate, the type of the target object is easier to judge, and the size of the disease is more accurately measured.

The invention discloses an oral cavity panoramic X-ray image tooth segmentation method based on deep learning. The method comprises the following steps: preprocessing data of an oral cavity panoramic X-ray image; designing and constructing an oral cavity panoramic X-ray image segmentation network model; constructing an evaluation system of the instance segmentation model; constructing a self-training iterative model method; training and obtaining an oral tooth segmentation model with the optimal precision on the verification set; inputting a label-free oral panoramic X-ray image into the trained oral tooth segmentation model, and reasoning the image to complete mask segmentation; and the oral tooth reasoning result is visualized. According to the method, the segmentation model with excellent performance can be obtained only by publicly annotating the data set without additional annotation of clinical experts, and the method has the advantages of high efficiency, high precision, good objectivity, stable repeatability and the like.

The invention discloses a chromosome segmentation method, system and device based on chromosome clusters and a medium, and belongs to the technical field of biomedicine and artificial intelligence in bioinformatics, and the segmentation method comprises the steps: carrying out the labeling processing of a chromosome original image, sequentially constructing a chromosome cluster recognition data set for different tasks, and training a chromosome cluster recognition network; constructing a chromosome cluster classification data set, and training a chromosome cluster classification network; constructing a chromosome cluster segmentation data set, and training a chromosome cluster segmentation network; inputting a chromosome original image, and performing reasoning by using the trained chromosome cluster recognition network, chromosome cluster classification network and chromosome cluster segmentation network in sequence to obtain a chromosome segmentation result; according to the invention, the chromosome can be automatically and accurately identified and segmented from the observation image; and by using the architecture based on the chromosome cluster, the probability of missing detection of the chromosome is reduced, and the downward compatibility of the network to the image resolution is high.

The invention provides a deviation defect detection method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a pre-labeled defect image, obtaining a training set, processing the defect image, obtaining processing data, optimizing an initial loss function of a pre-constructed initial segmentation model through the processing data, and obtaining a deviation defect detection result. Obtaining a target loss function, and training the initial segmentation model to obtain a target segmentation model; inputting a to-be-detected image into the target segmentation model to obtain edge positions of a part area of the to-be-detected part and a reference area of the to-be-detected part; and determining whether the to-be-detected part has the deviation defect or not based on the part area of the to-be-detected part and the edge position of the reference area of the to-be-detected part. According to the method, the loss function of the existing segmentation model is updated to obtain the target segmentation model, so that the target segmentation model can be used for accurately extracting and segmenting the edge region of the product, and the accuracy of detecting the deviation defect is improved.

The invention discloses a mushroom yield estimation method based on three-dimensional reconstruction lossless phenotype parameter extraction, belongs to the technical field of agricultural informatization and intelligentization, and solves the problem of low mushroom yield estimation accuracy. The method comprises the following steps: performing point cloud three-dimensional reconstruction on shiitake mushrooms by using a region of interest to generate dense point cloud data; preprocessing the dense point cloud data; based on an improved CP-PointNet + + model and a clustering algorithm, segmenting mushroom point cloud, extracting multi-dimensional key phenotypic parameters of pileus diameter, thickness and stipe height, and covering main features of mushroom phenotypic information; the improved PointNet + + network comprises the following steps: adding a CBAM module to an MLP layer of the PointNet + + network, and replacing common convolution with partial convolution PConv; and taking the phenotypic parameters of the shiitake mushrooms as input variables of the neural network model, and predicting the shiitake mushroom yield. The method is suitable for phenotypic character analysis and breeding research of edible mushrooms such as shiitake mushrooms and the like.

The invention relates to an adaptive stacked workpiece point cloud data synthesis and instance segmentation method and device, and the method comprises the steps: collecting the point cloud data of a single workpiece, and carrying out the automatic reverse modeling through a feature preserving reconstruction algorithm, so as to obtain a CAD model of the single workpiece; establishing and training a stacked workpiece instance segmentation model comprising a backbone network module, a semantic prediction module, a central point offset prediction module, a binary clustering module, a centripetal offset module and a voting module; and completing the point cloud data and refining edge details through a point cloud data completion and detail feature preserving algorithm, and inputting the processed point cloud data into the trained instance segmentation model to realize accurate instance segmentation of the stacked workpiece point cloud data. According to the invention, through technologies of adaptive point cloud data synthesis, deep learning instance segmentation, point cloud data completion and the like, the quality and segmentation precision of the point cloud data are significantly improved, and the method is suitable for workpiece identification and operation tasks in a complex industrial environment.

The invention provides an accurate target identification and path planning method and system based on point cloud segmentation, and relates to the technical field of robot navigation and path planning, and the method comprises the steps: firstly, collecting three-dimensional point cloud data of a target region, and setting related parameters; then, segmenting the point cloud by using a region growing algorithm to obtain a plurality of target regions; thirdly, extracting target features based on a segmentation result, and performing target recognition by using a support vector machine (SVM); then, calculating an optimal path from the starting point to the target point through an improved dynamic weighting A * algorithm; and finally, introducing time sequence information in the path planning process to realize real-time path optimization. Through the technical scheme of the invention, the accuracy of target identification is improved, the efficiency of path planning is optimized, the robot can better navigate in a complex environment, and the method has important application value.

The invention discloses a road sign defect detection method and system based on Transform and a medium, and the method comprises the steps: firstly, obtaining an image, carrying out the preprocessing of the image, carrying out the road sign region segmentation of the image through employing a Mask2Former algorithm, and generating a Mask contour of a road sign; based on the minimum enclosing rectangular frame of the road identification area, segmenting the original image to obtain a plurality of sub-images containing the identification area; then, a YOLO-WORLD multi-mode model is used for conducting defect detection on the sub-graph rectangular frame of the road mark, the defects of the road mark are detected on the sub-graph rectangular frame, and a defect positioning frame is generated; and finally, in combination with the Mask contour, filtering the pixel positions of the defects, and filtering out the defects outside the road marking area. According to the method, two excellent algorithm models of Mask2Former and YOLO-WORLD are creatively combined, accurate road marking area segmentation in a complex environment is realized, the detection precision and robustness are improved, the false alarm rate is remarkably reduced, and the detection reliability is improved.

The invention provides a forearm blood vessel image segmentation method and device based on an HAMU-Net deep learning network, and relates to the technical field of medical image processing. The method comprises the following steps: performing forearm blood vessel image acquisition through a near-infrared acquisition device to obtain an original image; based on a Gaussian filter, according to a preset background gray threshold, preprocessing the original image to obtain a processed image; based on the Reinforcement-CLAHE technology, according to the processed image, a residual error convolution automatic encoder is used for carrying out multi-stage image enhancement processing, and an enhanced image is obtained; the method comprises the following steps of: constructing an HAMU-Net blood vessel segmentation model on the basis of a Hybrid Action module, a Triplet Action module, a Spatial Action module and a U-Net network structure; and inputting the enhanced image into the HAMU-Net blood vessel segmentation model to obtain a blood vessel segmentation image. The near-infrared forearm blood vessel image segmentation method is an efficient and accurate near-infrared forearm blood vessel image segmentation method based on the mixed attention module.

The invention relates to the field of network live broadcast, and discloses a live broadcast stream image segmentation method and device, equipment and a medium, and the method comprises the steps: sequentially calling image frames from an image sequence of a live broadcast stream as current image frames; a depth estimation segmentation model is called, a segmented figure mask corresponding to the figure image is determined based on the current image frame, and the depth estimation segmentation model is supervised and trained by using scene depth information; performing inter-frame smoothing processing on the segmented figure mask of the current image frame by using the practical figure mask iteration of the historical image frame to obtain a practical figure mask of the current image frame; and determining a figure image in the current image frame according to the practical figure mask of the current image frame. By fusing the depth information and the inter-frame smoothing technology, real-time, accurate and stable live broadcast stream image segmentation is realized, the live broadcast watching experience is improved, and the industry technology progress is promoted.

The invention relates to the technical field of medical imaging, in particular to a medical ultrasonic image segmentation method and system based on adaptive feature fusion. The method comprises the following steps: acquiring medical ultrasonic image historical data, and performing ultrasonic medical history feature extraction according to the medical ultrasonic image historical data so as to obtain medical ultrasonic image medical history data; constructing an ultrasonic image disease classification model according to the medical history data of the medical ultrasonic image so as to obtain the ultrasonic image disease classification model; a patient medical record ultrasonic image is obtained, zero pixel points are removed according to the patient medical record ultrasonic image, and therefore a patient non-zero pixel point ultrasonic image is obtained; and performing ultrasonic layered tissue analysis according to the non-zero pixel point ultrasonic image of the patient to obtain tissue lesion area data. The medical ultrasonic image segmentation accuracy is improved based on the medical imaging technology.