139 results about "Lesion type" patented technology

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 discloses an intelligent detection method and device for fusing a medical image learning image, and relates to the technical field of medical image processing. The method comprises the following steps: acquiring and preprocessing a bimodal medical image, and extracting a feature map through multi-scale decomposition; constructing a cross-modal correlation model, and setting a modal attention mechanism (embedding anatomical structure prior guidance feature complementation) and a morphological attention mechanism (setting lesion morphological constraint weight); the method comprises the following steps: collecting multiple types of image samples, pairing according to a focus form and an imaging mode to construct a bimodal joint data set, and correlating and labeling to generate a training data set with modal attributes; after a multi-stage iteration training model, inputting the preprocessed image to carry out feature fusion so as to obtain a fused image; and generating a lesion probability graph according to the fused image, positioning a lesion area through multi-threshold segmentation, and outputting a detection result. The system comprises a data acquisition module, a preprocessing module and the like. The method improves the accuracy and reliability of medical image detection, and is suitable for clinical multi-modal image analysis.

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 image analysis, in particular to an alimentary canal lesion analysis method and system based on image recognition, based on an endoscope image of an alimentary canal of a patient, the edge intensity of pixel points in the image is evaluated through image analysis, and a suspected lesion area in the image is recognized by adjusting an edge detection threshold value; the non-suspected lesion area and the suspected lesion area are distinguished, and a digestive tract area distinguishing result is obtained. According to the method, the identification process of the suspected lesion area and the non-suspected lesion area is optimized by dynamically adjusting the edge detection threshold value, the shape, size and color of the suspected lesion area are evaluated, and the suspected lesion area is matched with the known lesion type in the database, so that the lesion type identification accuracy is improved; and the image data corresponding to the lesion type is extracted, data support is provided for subsequent medical personnel to confirm the lesion type, and the evaluation of the healthy tissue is refined by analyzing the deviation between the non-suspected lesion area and the normal color data.

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 belongs to the technical field of image detection, and discloses an enteroscopy auxiliary diagnosis method and system based on artificial intelligence, and the method comprises the steps: obtaining enteroscopy image data in an enteroscopy process, and synchronously extracting corresponding auxiliary collection information from the enteroscopy image data; caching the enteroscope image data and the auxiliary acquisition information frame by frame and keeping timestamps aligned; performing standardized correction and adaptive enhancement processing on the enteroscope image data after timestamp alignment; a self-adaptive gamma dynamic enhancement mechanism is introduced, and intestinal tract wrinkles are reserved to obtain an enhanced image; lesion positioning and category screening are conducted on the enhanced image through the target detection model, multi-scale feature aggregation and time sequence consistency constraint are conducted in combination with auxiliary collection information, and lesion candidate areas are generated; performing morphological, texture and boundary structure analysis on the lesion candidate region, and extracting lesion morphological features; and the efficiency and the reliability of enteroscopy diagnosis can be improved.

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 diabetic retinopathy total fundus dynamic labeling system, and relates to the technical field of medical image processing. The system comprises a data acquisition module, an image labeling module, a multi-mode integration display module and a user interaction module. According to the system, the diabetic retinopathy marking model is utilized to realize lesion area identification and lesion type marking on various types of fundus image data, so that a doctor can clearly and quickly know the progress of an illness state without artificial experience; the full fundus image data marked with the lesion area and the lesion type, the image data of the posterior pole and the macular area and the retinal blood vessel image data are fused, the lesion area and the lesion type of any area of the full fundus of the user to be detected can be displayed on a display interface, a visual blind area is eliminated, and the detection accuracy is improved. The user can view any interested area of the whole fundus, so that comprehensive analysis of the diabetic retinopathy is realized, and the diagnosis and treatment efficiency of the diabetic retinopathy is comprehensively improved.

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 discloses an intelligent pathological report automatic generation system based on data analysis, which relates to the technical field of pathological data management and comprises a cloud platform, a data acquisition module, a feature processing module, a classification and identification module and a report generation module. The data acquisition module constructs a data filtering network to receive pathological section images, the feature processing module performs image processing on the pathological section images to generate pathological image data clusters and judges whether the pathological image data clusters meet preset feature identification conditions or not, if yes, pathological section features are extracted, and if not, secondary image processing is performed; inputting all pathological section features into a pathological feature library by a classification and recognition module so as to classify and recognize a lesion region, a lesion type and a lesion degree of a patient corresponding to each pathological section feature, and integrating the lesion region, the lesion type and the lesion degree into a pathological information set; and the report generation module generates a pathological report of each patient according to the pathological information set, stores the pathological report into the diagnosis auxiliary database, and intelligently pushes the pathological report.

The invention relates to the field of medical image processing, in particular to a gastric cancer precancerous lesion progress risk assessment system based on time sequence image analysis, which comprises a time sequence gastroscope image registration module, a lesion curved surface dynamic representation module, a multi-dimensional feature manifold construction module, a risk dynamic prediction module and a virtual pigment endoscope enhancement module, according to the method, a differential geometry theory is introduced, the gastric mucosa surface is modeled as a Riemannian curved surface, and Gaussian curvature, average curvature and other characteristics are extracted; constructing a feature manifold space by adopting a manifold learning method, and analyzing a lesion state evolution trajectory through geodesic distance; and predicting the time for the lesion to reach a high-risk state based on the probability density distribution and the evolution vector field on the manifold. According to the method, accurate characterization of gastric cancer precancerous lesion morphological characteristics, quantitative analysis of dynamic evolution and accurate prediction of risk progress are realized, a scientific basis is provided for clinical follow-up visit decisions, and the method has the remarkable advantages of improving the early intervention rate, optimizing medical resource allocation and the like.

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 discloses a weighted prototype network-based uterine cavity lesion image classification method. The method comprises the following steps of 1, collecting a uterine cavity lesion image set; 2, classifying the uterine cavity lesion image set, and aligning the resolution and the visual field of the image; 3, establishing a data set; 4, self-adaptive data enhancement is carried out, and the number of training data is increased; 5, training a weighted prototype network WPN, and evaluating the performance of the method; 6, inputting a test image into the trained WPN network model, and calculating the distance between the features of the input image and each type of prototype; and 7, outputting the category of the uterine cavity lesion image. According to the method, prototype parameters of different types of lesion images are generated by utilizing the uterine cavity lesion images through a deep learning method, after the WPN network model training is completed, the image lesion types can be accurately output in real time by inputting the uterine cavity lesion images, and disease diagnosis of clinicians is facilitated.

The invention discloses a tooth image detection device and method based on a YOLOv5 model, applied to the field of medical images, and the method comprises the steps: obtaining a pre-trained tooth image detection model; according to the tooth image detection model, an attention mechanism is introduced on the basis that a YOLOv5 convolutional neural network is used as a framework, a HardSwsh activation function is used as an activation function, and an EIOU loss function is used as a loss function in a model training process; inputting a tooth image to be detected into the trained tooth image detection model, and detecting whether a focus area exists or not; if yes, displaying the lesion area in a rectangular frame labeling mode, and displaying lesion information; the lesion information at least comprises lesion type, number, size and position information. According to the method, the tooth image detection speed and precision of the YOLOv5 convolutional neural network are improved, and the detection effect is obviously improved.

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 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.