128 results about "Lesion types" patented technology

The invention relates to the technical field of remote medical treatment, in particular to a remote ultrasonic diagnosis support system based on cloud computing, which comprises an echo feature analysis module, a dynamic focus screening module, a case image comparison module, a focus trend classification module and a remote diagnosis support module. According to the method, by analyzing echo signals in a remote ultrasonic image data stream and calculating the echo change rate between image frames, the dynamic characteristic capturing capacity of a lesion area is improved, calculation of a gray gradient change value is combined, a suspected lesion area conforming to a change rate stable interval is screened, lesion shape edge curve contour parameters are utilized, and the dynamic characteristic capturing capacity of the lesion area is improved. The method comprises the following steps: performing matching analysis with an ultrasonic case image database, enhancing the accuracy of lesion category attribution, analyzing the morphological change trend of a lesion area, combining trend stability index calculation, realizing lesion evolution classification, ensuring the reasonability of lesion type division, and marking a progressive lesion area, so that remote diagnosis is more intuitive; and the auxiliary decision-making capability is improved.

The invention relates to the technical field of medical image processing, in particular to a CT image intelligent analysis system for pneumonia auxiliary screening. The method comprises the following steps: firstly, preprocessing a chest CT image and detecting a candidate focus area; secondly, extracting a topological feature, a deep convolution feature and a texture statistical feature based on a persistent coherence theory from each candidate focus, and performing feature fusion through a multi-head self-attention mechanism to generate a unified focus representation vector; mapping the lesion characterization vectors to a pre-constructed radiology knowledge graph, adopting a graph neural network for reasoning, and outputting the pneumonia suspected probability and lesion classification of each lesion; and finally, performing fusion and uncertainty quantification on the analysis results of the plurality of focuses by adopting an evidence theory, and generating a comprehensive screening report. According to the method, complex-form lesions are effectively identified through topological features, accurate identification of lesion types is realized through knowledge graph reasoning, and diagnosis uncertainty quantification is provided through an evidence theory.

The invention relates to the technical field of medical image processing, in particular to a method and system for identifying odontogenic jaw cyst and tumor based on deep learning, and the method comprises the steps: obtaining original jaw CBCT image data, carrying out the preprocessing, and generating a combined annotation data set containing lesion types, tooth root absorption form annotations and CT value annotations; constructing a multi-branch neural network model, and training the multi-branch neural network model by using the joint labeling data set to obtain an odontogenic jaw lesion recognition model; and obtaining jaw CBCT image data of a target patient, inputting the jaw CBCT image data into the odontogenic jaw lesion recognition model, and outputting lesion type classification results, tooth root absorption forms and bone destruction volumes. The diagnosis capability of minimal lesions can be improved, missed diagnosis is reduced, whole-process auxiliary diagnosis from qualitative diagnosis to quantitative analysis is realized, and the diagnosis speed is increased.

The invention discloses a cerebral vessel anomaly analysis method based on a transcranial Doppler ultrasound image, which comprises the steps of TCD image data acquisition, TCD image space-time filtering preprocessing, automatic blood vessel positioning and three-dimensional modeling, the method comprises the steps of generating a standardized blood vessel segmentation model and a static blood vessel network graph, extracting blood flow quantitative parameters and spectrum qualitative features, analyzing multi-blood vessel space correlation features through a graph attention network, performing primary classification on blood flow states, performing secondary classification on lesion types, making risk grading judgment, generating intervention suggestions and outputting a structured report. By setting an automatic parameter measurement method, the manual operation time of doctors is shortened, and the speed and accuracy of lesion judgment are improved through automatic standardization judgment of frequency spectrum forms and association of multi-dimensional parameters. Meanwhile, the experience requirement of doctors for judging the lesion is reduced, and reference is conveniently provided for batch screening of primary hospitals.

The invention relates to the technical field of image processing, in particular to a clinical ultrasonic image auxiliary screening system based on deep learning. The system comprises an ultrasonic image preprocessing module, a lesion area segmentation module, an image lesion marking module and a lesion auxiliary screening module, and can obtain a clinical ultrasonic image and perform graying processing and neighborhood gray level equalization processing to generate a clinical ultrasonic equalization image; performing pixel normalization and tissue boundary fuzzy-based lesion region segmentation on the clinical ultrasonic equalization image to generate clinical ultrasonic image lesion region blocks; carrying out image lesion marking through the clinical ultrasonic image lesion area block to generate a clinical ultrasonic lesion marking image set; and constructing an image lesion auxiliary screening model and carrying out lesion auxiliary screening so as to output lesion positions, lesion types and lesion severity corresponding to lesions on the clinical ultrasonic image. According to the invention, accurate analysis of clinical ultrasonic images can be realized, so that lesion screening can be efficiently completed in an assisted manner.

The invention relates to the field of CT (Computed Tomography) images, and discloses a deep learning-based CT image feature extraction and analysis method, which comprises the following steps of: performing de-noising processing, image normalization and data enhancement on an input head CT image; using a convolutional neural network to carry out preliminary detection of a focus area on the preprocessed CT image; using a generative adversarial network to carry out fine segmentation on the preliminarily detected lesion area; carrying out feature extraction on the lesion region after fine segmentation, and inputting the extracted features into a classifier to carry out lesion type classification; post-processing the classification result, and superposing the segmented focus area on the original CT image to generate an image with a focus mark; and evaluating the model, carrying out quantitative analysis on the model by adopting cross validation, and carrying out iterative optimization according to an evaluation result. The method has the advantages that stable extraction of the focus area is ensured, and the diagnosis efficiency and reliability are improved.

The invention relates to the technical field of medical image analysis and processing, and discloses a complex aortic lesion intelligent analysis method and system and a storage medium. The method comprises the following steps: collecting a plurality of aorta images of multiple image types containing different lesion types, collecting lesion information as label information, and labeling the lesion aorta images based on the label information; extracting a lesion area and a key area, generating first training data and training a first recognition model; acquiring to-be-analyzed aortic images of multiple image types of a patient, performing enhancement processing, using a trained first identification model to identify each key region in the to-be-analyzed aortic images, acquiring a lesion score of each lesion type, calculating a first lesion score of each lesion type of the to-be-analyzed aortic images, and performing image processing on the to-be-analyzed aortic images according to the first lesion score; and determining a corresponding lesion type based on the first lesion score. According to the method, various image types can be effectively analyzed, and the recognition accuracy of aortic lesion types is improved.

The invention discloses a lightweight hysteroscope image diseased region detection model construction method, which comprises an input layer, a multi-scale feature pyramid module, a lightweight backbone network module, a target detection head module and an output layer, performing hierarchical feature extraction and cross-layer feature fusion through a multi-scale feature pyramid module to obtain fused feature maps of different scales; the fused feature maps are input into a lightweight backbone network module and are further processed through a deep separable self-association mechanism and a dynamic region attention mechanism, and the capturing capability of the features of the lesion site is enhanced; the processed feature map is input into a target detection head module, multi-scale feature matching detection and self-adaptive anchor frame generation are carried out, and the category and the position of the diseased region are predicted; finally, the output layer outputs a detection result of the lesion part, and the detection result comprises lesion types and corresponding bounding box information.

The invention discloses a medical image curative effect evaluation system based on artificial intelligence, and the system is characterized in that an acquisition module obtains a clinical vascular medical image, builds an image database according to the clinical vascular medical image, and builds a high-resolution vascular three-dimensional image according to the image data in the image database; a positioning recognition module receives the blood vessel three-dimensional image and inputs the blood vessel three-dimensional image into a trained blood vessel positioning model, and the blood vessel positioning model automatically recognizes the blood vessel position and level in the blood vessel three-dimensional image; the evaluation module is used for cutting and calculating the blood vessel three-dimensional image according to the recognized blood vessel position and level and a preset size, and evaluating whether the lesion position and lesion type of the blood vessel with blood vessel stenosis, blood vessel dilatation and blood vessel thickening occur or not according to a calculation result; the technical problem that the prior art is difficult to accurately evaluate the curative effect of vascular diseases is solved.

The invention provides a target detection-based blood vessel feature analysis method and device, equipment and a medium, and the method comprises the steps: carrying out the processing of an input blood vessel image through an initialized feature extraction module, generating a pre-training model, carrying out the feature extraction of the blood vessel image through the pre-training model, and obtaining image feature data; inputting the lesion area into an analysis module, and performing image segmentation on the lesion area through the analysis module to obtain segmented image features; the segmented image features comprise image data of a calcified region and / or a non-calcified region; and respectively inputting the segmented image features into a calcified plaque analysis module and a non-calcified plaque analysis module to obtain the type of a vascular lesion area, further analyzing the density distribution of mixed plaques to determine whether the mixed plaques are in a splicing type or a uniform type, and finally generating an analysis result containing the lesion type and the stenosis degree. According to the invention, accurate positioning, segmentation and classification of vasculopathy can be realized without labeling data, and the automation level and accuracy of vasculopathy diagnosis are improved.

The invention relates to a reasoning enhanced vision-language large model training and image processing method, and the training method comprises the following steps: obtaining an ultra-wide-angle fundus image as an input image, and taking the manual annotation DR classification, the manual annotation lesion type and the clinical background of the ultra-wide-angle fundus image as cue words; utilizing a vision-language model with reasoning ability to generate reasoning enhanced image description and DR classification and lesion types obtained through reasoning; and taking the generated image description and the DR classification and lesion type obtained by reasoning as instructions, constructing a reasoning enhancement instruction data set in combination with the ultra-wide-angle fundus image, and finely adjusting the reasoning enhancement type vision-language large model. Compared with the prior art, the method has the advantages of being capable of effectively integrating clinical knowledge, high in recognition accuracy, high in interpretability and the like.

A method, system, device and storage medium for measuring and classifying CT-FFR, dividing lesions into different types according to coronary artery computed tomography angiography (CCTA) images, determining lesion lengths at lesion positions according to different lesion types, and determining the lesion lengths at the lesion positions according to different lesion types. Then, the measurement range of computed tomography fractional flow reserve is determined according to the focus lengths of different lesion types, a statistical matrix is established to calculate the numerical values of all computed tomography fractional flow reserve CT-FFR in the measurement range, and finally, hemodynamic classification is conducted on calculation results. According to the CT-FFR evaluation method, the accuracy and the stability of CT-FFR evaluation are enhanced by carrying out pathological change specificity classification on the pathological change in the haemodynamics sense, interference is reduced, and a more reliable solution is provided for clinicians and patients.

The invention relates to a pancreatic lesion resection type auxiliary decision making system, method and device, computer equipment and a storage medium. The system comprises a processor which executes the following steps: acquiring a three-dimensional medical image of the pancreas, wherein the three-dimensional medical image of the pancreas comprises a focus; the number of lesions and lesion properties are recognized, the number of the lesions is one or more, and the lesion properties comprise at least one of a first lesion type and a second lesion type; and constructing a decision tree model according to the number of the lesions, the lesion properties and the surgical function to generate a resection auxiliary decision corresponding to the lesions. By adopting the system, the specificity of different pancreatic lesions can be fully considered, a lesion resection type decision-making tree model is constructed according to the number of the lesions, lesion properties and a surgical function, corresponding resection type auxiliary decisions are generated by utilizing judgment branches of the decision-making tree model according to the specific conditions of different lesions, and the lesion resection type auxiliary decisions are obtained. And a doctor is accurately, efficiently and intelligently assisted in selecting a pancreatic focus resection mode.

The invention discloses a medical graphic image artificial intelligence modeling and recognition system, and particularly relates to the field of intelligent modeling and recognition, and the system comprises a data collection module, an image data preprocessing module, an image segmentation module, a feature extraction module, an active learning model construction module, and a recognition and diagnosis module. The method comprises the following steps: acquiring a CT image of a patient, preprocessing, including denoising and enhancing, segmenting the image, and distinguishing a lesion region to be determined from a normal region; key features of the lesion area are extracted by using a deep learning algorithm, and a lesion value is calculated to determine the lesion area; thirdly, inputting the lesion features into an active learning model for training, selecting the most valuable sample label through iteration, and optimizing the model performance; and finally, inputting a to-be-recognized image into the trained model, and outputting a lesion type, a position, a size and a preliminary diagnosis report. The system also has a visual display function, highlights and marks a lesion area, and provides feedback and optimization circulation.

The invention relates to an intelligent target detection method and system for a gastroscope image, and the method comprises the steps: carrying out the multispectral decomposition of an obtained gastroscope image, and generating a standardized image in combination with a dynamic domain adaptation strategy; based on the standardized image, adopting a multi-scale geometric perception feature extraction network to extract fusion features; carrying out anatomical position sensing processing on the fusion features to generate position sensing features with anatomical region identification capability; on the basis of the position sensing features, morphologically-guided multi-task target detection and classification are executed, and a multi-task detection result is obtained; the multiple tasks comprise focus detection, focus boundary segmentation and focus type classification; aiming at a multi-task detection result of continuous image frames in the gastroscopy process, applying a time sequence consistency constraint, and generating a space-time optimization result; and constructing an uncertainty perception mechanism based on a space-time optimization result, and outputting a calibrated target detection result. The precision of gastroscope image detection can be improved.

The invention discloses an automatic classification method for hepatic echinococcosis based on an ultrasonic image. The method comprises the following steps: in a preprocessing stage, denoising, normalization and size standardization are carried out on an original image, and quality gating is executed; in the segmentation stage, focus positioning and pixel-level segmentation are completed on the standardized image, and a stable mask is obtained through'opening-closing 'morphology and coverage rate constraint; in the feature and initial judgment stage, multi-scale feature extraction is carried out on a focus area, and uncertainty is calculated; in the fine judgment stage, features are dynamically weighted and fused according to reliability, and focus types and activity grades are output; in the closed-loop stage, self-adaptive write-back of the model parameters, the segmentation threshold and the enhancement gain is driven by the compound loss; in the result stage, temperature scaling and expected calibration error evaluation are implemented, and a structured report containing type, classification, confidence and parameter snapshots is generated.

The invention provides a dual-order optimization self-adaptive diabetic mesh screening model generation method and lesion recognition equipment, and relates to the technical field of diabetic mesh screening, and the method comprises the following specific steps: collecting a plurality of fundus images and corresponding medical record data, carrying out fine processing and detailed labeling, presetting a machine learning detection model for training, and carrying out the recognition of the fundus images and the corresponding medical record data. The fundus image and the medical record data of the samples in the training set are used as input features, the corresponding label content is used as an output label, and dual-order optimization and self-adaptive adjustment are adopted to enhance the recognition capability of the model on lesion features; screening a model according to a category consistency coefficient between lesion types, a feature deviation coefficient between lesion features and a logic consistency coefficient of logic rules, and screening a dual-order optimization adaptive sugar mesh screening model by using samples in a test set. Clinical diagnosis can be accurately assisted, the missed diagnosis and misdiagnosis rate is effectively reduced, the problem that the feature recognition precision of a traditional model is insufficient is solved, it can be ensured that prediction conforms to clinical logic, and the reliability of the model is improved.

The application provides a blood vessel lesion determination method and device, electronic equipment and storage medium. The determination method comprises: inputting acquired no-label images and labeled images as input images into a blood vessel lesion analysis model in a single-alternating input manner; if the input image is a no-label image, training the blood vessel lesion analysis model according to a reconstruction loss function; if the input image is a labeled image, determining whether the reconstruction loss function, a lesion type loss function and a lesion degree loss function simultaneously satisfy a target condition, and if the target condition is satisfied, obtaining a trained blood vessel lesion analysis model; and determining a lesion type result and a lesion degree result of a blood vessel image according to the trained blood vessel lesion analysis model. The technical solution provided by the application can reduce manual labeling operations, reduce the workload of doctors, and ensure the accuracy of blood vessel lesion determination.

The invention provides an image detection method and device, equipment and a storage medium, and the method comprises the steps: obtaining a detection image obtained through plain-scan CT, and extracting a target body part image from the detection image; performing first image classification segmentation processing on the target body part image through a first image detection model to determine that a first target lesion type and a lesion area corresponding to the first target lesion type exist in the target body part image; and performing second image classification segmentation processing on the target body part image through a second image detection model to determine a second target lesion type and a lesion area existing in the target body part image, the second target lesion type being a sub-category of the first target lesion type. Through cooperation of the two image detection models, refined detection of an image containing a lesion type and a lesion area can be realized.

The application discloses a kind of intraoperative tumor rapid classification system based on hyperspectral technology, comprising: image pre-processing module, for the hyperspectral image of several frequency bands of acquired tumor lesion tissue is pre-processed;Tumor lesion area identification module is used to select the hyperspectral image corresponding to multiple preferred frequencies respectively, fusion obtains fusion spectrum image, tumor target detection is carried out based on the image, and tumor lesion area is identified;Tumor lesion type identification module is used to carry out multi-pixel sampling to the tumor lesion area identified, obtains the frequency characteristics of each preferred frequency of each sampling point, and respectively input benign and malignant tumor identification model, respectively output benign and malignant possibility matrix, to identify tumor lesion type.The present application discriminates tumor benign and malignant based on tumor lesion tissue hyperspectral image, realizes the rapid classification of intraoperative tumor lesion type, reduces the rapid identification time, improves the accuracy of identification result while improving the identification efficiency.

The present invention relates to a weakly supervised learning diabetic retinopathy grading and lesion identification method and system, the method comprising: S1: inputting a diabetic retinopathy fundus color image into a CNN network to obtain a feature map F; inputting F into a lesion point capture module based on weakly supervised object positioning, selecting to enter a branch module, calculating a score map, multiplying the score map with the feature map F to obtain a new feature map F'; S2: using an optimal network structure search module based on reinforcement learning to calculate the insertion position, selection probability, feature discarding threshold and retention threshold of the weakly supervised lesion point capture module in the CNN network, and finally outputting the feature map F. NA S3: Lesion attribute prediction and disease classification are obtained through attribute mining and lesion identification modules. S4: Based on the results of S3, the final lesion identification result is obtained through multiple iterative erasure calculations. The method provided by the present invention can improve the effectiveness of lesion capture and use lesion mining methods to determine lesion type, providing a basis for disease classification.

The application discloses a brain injury identification method and an identification system. The identification system identifies the injury of a patient according to the identification method. The identification method comprises the following steps: firstly, the electrical impedance tomography (EIT) of the brain of the patient is performed to obtain the EIT image of the brain of the patient; then, the region with abnormal resistivity distribution in the EIT image of the brain is taken as a reference for scanning of a near-infrared spectrometer, and a scanning path of the near-infrared spectrometer is planned; finally, the near-infrared spectrometer is scanned along the scanning path, and the lesion range and the lesion type are determined according to the obtained near-infrared spectral data. The electrodes need not be arranged on the whole head of the patient, and the automatic and rapid identification of the lesion range and the nature of the brain injury is realized.

The present disclosure relates to a lumbar vertebra lesion identification method, device, medium and electronic equipment, wherein the method comprises: determining a lumbar vertebra image of a user, the lumbar vertebra image being a T2 weighted magnetic resonance image; inputting the lumbar vertebra image into a target detection model to obtain a lesion category of the lumbar vertebra of the user, wherein the target detection model comprises a down-sampling layer and a pooling layer, the down-sampling layer is used to extract feature data of the lumbar vertebra image, and the pooling layer is used to classify the feature data to obtain the lumbar vertebra lesion category of the user. Compared with the way of judging the lumbar vertebra lesion by artificial judgment and identifying the lumbar vertebra lesion by machine learning method in the related art, the error of identifying the lumbar vertebra lesion type can be reduced, and thus the accuracy of identifying the lumbar vertebra lesion can be improved.

The invention provides a face acne skin lesion detection matching method, system and device, and relates to the field of image processing, and the method comprises the steps: calling a face key point detection interface, and obtaining face key point coordinates on face images at different angles; obtaining a first mapping transformation reference point of each face key point corresponding to the side face and the front face; according to the first mapping transformation reference point, generating a perspective transformation matrix of mapping the side face to the front face; according to the perspective transformation matrix and the human face key point coordinates, mapping the skin lesion detection frame on the side face to a position corresponding to the front face; matching the skin lesion detection frame on the front face with the skin lesion detection frame mapped to the front face by the side face; and unifying the skin lesion type labels of the skin lesion with the front face and the side face successfully matched. According to the invention, the accuracy of acne evaluation results can be improved.

The invention discloses a deep learning-based digestive tract auxiliary detection method and device, and relates to the technical field of auxiliary detection, and the method comprises the steps: carrying out the original image collection of the digestive tract of a patient through endoscope equipment based on an event-driven sampling strategy, carrying out the denoising, size adjustment and quality improvement processing of the original image, and obtaining a preprocessed image; using Canny edge detection to extract a suspected lesion area of the preprocessed image; in combination with a super-resolution generative adversarial network and a self-attention mechanism, performing super-resolution reconstruction and local enhancement on the preprocessed image to obtain a high-definition enhanced image; and based on the lesion segmentation mask and the high-definition enhanced image, lesion types are classified by using a pre-trained feature extraction network. According to the method, the super-resolution generative adversarial network and the self-attention mechanism are combined, super-resolution reconstruction and local enhancement are performed on the preprocessed image, image details are enhanced, especially the definition of a minimal lesion area is enhanced, and the omission ratio is effectively reduced.

The application discloses an auxiliary identification method for brain inflammatory pseudotumor MRI images based on machine learning, and belongs to the technical field of medical image processing, and comprises the following steps: acquiring preoperative magnetic resonance imaging (MRI) data of a brain lesion of a to-be-measured individual, and performing standardization preprocessing on the MRI data to obtain a standardized brain MRI image; a lesion target region containing a tumor core region and a peritumoral edema region is segmented on the standardized brain MRI image, and a training set, a verification set and a test set are generated based on the lesion target region; a lesion type auxiliary identification model is constructed based on feature screening and feature topology graph attention network of SVM weights, and the training set and the verification set are used for training; the test set is input into the trained lesion type auxiliary identification model to obtain a lesion type identification result, and the problems of time-consuming and labor-consuming, low efficiency, omission of hidden dangers, low accuracy and the like in existing artificial visual measurement and traditional image processing measurement methods are solved.

The invention discloses a generative artificial intelligence cerebral artery lesion detection method based on plain-scan brain CT. The method comprises the steps that 1, a plain-scan brain CT image to be processed is acquired; 2, converting the plain scanning brain CT image into a CTA image through an adaptive noise elimination network; and 3, carrying out multi-modal lesion detection on the basis of the CTA image generated in the step 2. According to the method, a CTA image is generated through an adaptive noise elimination network (ANE-NET), and a real-time lesion feature analysis engine (RTAL-FE) is embedded in the generation process, so that real-time classification prediction of lesion types is realized. And furthermore, a detection result is output through a dynamic weight decision model (DWD-M), so that the generation quality and the detection efficiency are remarkably improved. The problems that a traditional method is low in generation quality and lags behind detection are solved, and the method is particularly suitable for low-dose and non-invasive cerebrovascular disease screening scenes and has important clinical application value.

This invention provides a deep learning-based intelligent analysis method for cardiovascular medical images, comprising the following steps: S1, heterogeneous data fusion and acquisition of multimodal medical images and physiological signals; S2, intelligent extraction and quantitative analysis of vascular structural features based on deep learning; S3, hemodynamic parameter modeling and risk region segmentation based on fused structural features; S4, texture feature mining and intelligent identification of lesion types in high-risk areas; S5, cardiovascular disease risk stratification prediction based on multi-dimensional feature fusion; S6, federated learning optimization and closed-loop update of the model. This invention's deep learning-based intelligent analysis method for cardiovascular medical images achieves, for the first time, heterogeneous fusion of medical images, millimeter-wave radar-PPG physiological signals, and clinical molecular data, and achieves precise spatiotemporal alignment through GPS-controlled crystal oscillators, overcoming the limitations of single image analysis and providing a more comprehensive feature foundation for subsequent feature extraction and risk prediction.

The present application relates to a kind of multi-center medical image lesion typing methods based on federal spectrum clustering, belong to medical image analysis technical field, including the following steps: S1: multiple medical institutions are regarded as federal learning client, and the medical image data and supporting clinical data of each client local are obtained and stored in each client local;S2: feature extraction and multimodal feature fusion are carried out in each client local, and multimodal lesion feature set is obtained;S3: center server and each client pass through iteration communication, and the multimodal lesion feature set is jointly clustered using federal spectrum clustering algorithm, until global clustering center converges;Only transmission encrypted statistical parameters in communication process;S4: according to clustering result, output lesion typing information.The present application solves the pain points of difficult data sharing and high privacy leakage risk in traditional centralized lesion analysis, and improves the lesion typing accuracy in heterogeneous data scenario.

The present invention discloses a gastrointestinal lesion localization method and device based on artificial intelligence, which relates to the technical field of image processing; local features of a first image are extracted to obtain a local feature map, global features of the first image are extracted, and a global feature map is obtained according to the local feature map and the global features; multi-scale feature extraction is performed on the local feature map to obtain a first feature set, and multi-scale feature extraction is performed on the global feature map to obtain a second feature set; a target feature map is obtained according to the first feature set and the second feature set; the target feature map is substituted into a preset database to determine the lesion type, and the lesion area is located according to the lesion type and the target feature map. By separately extracting local features and global features, it is possible to provide context information while capturing subtle changes in the lesion, helping to understand the position of the lesion in the entire image. Then, by capturing features at different scales, the accuracy and precision of gastrointestinal lesion localization are improved.