65 results about "Lesion types" patented technology

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.

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 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 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 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 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 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 application relates to the technical field of medical image processing, in particular to a CT image intelligent analysis system for pneumonia auxiliary screening. The present application firstly pre-processes chest CT images and detects candidate lesion regions; then extracts topological features, deep convolution features and texture statistical features based on persistent homology theory for each candidate lesion, performs feature fusion through a multi-head self-attention mechanism to generate a unified lesion representation vector; then maps the lesion representation vector to a pre-constructed radiology knowledge graph, performs reasoning using a graph neural network, and outputs the pneumonia suspected probability and lesion classification for each lesion; finally, the evidence theory is used to fuse and quantify the uncertainty of the analysis results of multiple lesions to generate a comprehensive screening report. The present application effectively identifies complex morphological lesions through topological features, accurately identifies lesion types through knowledge graph reasoning, and provides quantitative diagnosis uncertainty through the evidence theory.

This invention discloses a method and device for segmenting diabetic retinopathy based on feature interaction, comprising: constructing a feature interaction module for semantic relationship modeling of multiple lesions, embedding it into each ViT block encoding layer of the image encoder of the image segmentation model SAM, obtaining an image segmentation model FIASAM fused with the feature interaction module; training the image segmentation model FIASAM fused with the feature interaction module using fundus color image-mask annotation; acquiring fundus color images of diabetic patients, inputting them into the trained image segmentation model FIASAM fused with the feature interaction module, and generating image segmentation results containing all lesion types. This invention can enhance the feature interaction capabilities between different lesion types of diabetic retinopathy and between lesions and the background of fundus color images, thereby improving the accuracy and reliability of automatic segmentation of multiple lesions in fundus color images.

This invention relates to the field of medical data mining technology, specifically to an automatic analysis and classification system for pulmonary function test data based on big data. First, it calculates a single-channel weighted evolution index based on the variation deviation of multi-channel data time-series curves, capturing the dynamic evolution characteristics of each dimension within a continuous observation window. Then, it combines the variation trend deviation between multiple channels to accurately assess the feature response sensitivity of each dimension in response to changes in pathological states. Finally, by comparing the differences between current data and historical lesion characteristics, it quantifies the discriminative contribution of each dimension when considering historical data for each lesion type. This allows the weighted discriminative signal intensity obtained based on feature response sensitivity and discriminative contribution to more comprehensively evaluate the sample contribution of each pulmonary function test dimension. This results in higher accuracy in training the classification model using the weighted discriminative signal intensity as sample weights, thereby improving the accuracy of pulmonary function test data classification.

The application discloses an electronic medical record retrieval method and device. The method comprises the following steps: analyzing an imaging description text, determining a lesion type described by the imaging description text, and extracting an imaging sign description related to the lesion type from the imaging description text; extracting an imaging sign description related to the lesion type from the imaging description text; predicting a lesion type corresponding to a target object based on the imaging sign description; generating a retrieval prompt text based on the lesion type; retrieving a plurality of candidate medical record segments related to the lesion type based on the retrieval prompt text; and accurately retrieving medical record segments based on imaging characteristics to avoid omissions or false detections caused by ambiguous natural language expressions or incomplete retrieval conditions. Based on the importance values of the plurality of candidate medical record segments, the plurality of target medical record segments with the highest matching degree with the retrieval prompt text are selected from the plurality of candidate medical record segments, thereby improving the efficiency and accuracy of medical record retrieval.

This application provides a comprehensive non-destructive detection method for internal lesions in potatoes based on multispectral feature screening and deep learning. The method includes: S1 collecting potato samples and obtaining their spectral data; S2 selecting a combination of CARS-VIP and CARS-SPA screening adapted to absorbance spectra, and a dual-wavelength correlation coefficient method adapted to energy spectra, based on the spectral data, to obtain a subset of characteristic wavelengths; S3 constructing a three-level discrimination model based on the subset of characteristic wavelengths to identify various internal lesions in potatoes. This model sequentially distinguishes between healthy and diseased samples, further subdivides lesion types in diseased samples, and enhances discrimination through an improved residual neural network for early or boundary lesion samples. This method, through multi-strategy feature screening and a three-level discrimination model, achieves high-precision non-destructive detection of various internal lesions in potatoes, significantly improving the sensitivity of early, subtle lesion identification, and providing efficient technical support for quality control in the potato industry.

The invention discloses an intrahepatic cholangiocarcinoma or tumor conventional ultrasonic image auxiliary diagnosis method and system based on a Vision-RWKV framework, and relates to the technical field of medical image auxiliary diagnosis. Comprising the following steps: acquiring image data information which is conventional liver ultrasonic image data of a plurality of existing patients; constructing an intrahepatic cholangiocarcinoma or tumor identification model based on a Vision-RWKV architecture, wherein the model has a linear complexity bidirectional attention mechanism; training an intrahepatic cholangiocarcinoma or tumor recognition model based on a Vision-RWKV framework by adopting the image data information; and inputting a conventional liver ultrasonic image of a patient to be identified into the trained intrahepatic cholangiocarcinoma or tumor identification model to obtain an identification result, wherein the result comprises a lesion type, lesion position calibration and credibility. The method has the advantages that ultrasonic examination is simple, economical and easy to operate, and CT / MRI examination is high in accuracy, so that more and wider crowds are covered and served, missed diagnosis and misdiagnosis are reduced, and patients obtain good prognosis and life quality.

Disclosed are a method and apparatus for generating a lesion model of a tissue site, and a computer-readable storage medium. The method includes obtaining a determined lesion type of the tissue site, determining a plurality of determined sub-anatomical models from a model database of the tissue site based on the obtained determined lesion type, generating a lesion model of the tissue site based on the plurality of determined sub-anatomical models, and displaying the lesion model of the tissue site. Accordingly, the lesion model of the lesion type corresponding to the tissue site can be generated as disclosed herein.

The invention relates to the technical field of medical data mining, in particular to a lung function examination data automatic analysis and classification system based on big data, which comprises the following steps: firstly, calculating a single-channel weighted evolution index based on change deviation of a multi-channel data time sequence curve, and capturing dynamic evolution characteristics of each dimension in a continuous observation window; furthermore, the characteristic response sensitivity of each dimension in response to pathological state change is accurately evaluated in combination with data change trend deviation among multiple channels; and finally, by comparing the difference between the current data and the historical lesion features, quantizing the discrimination contribution degree of each dimension when the historical data of each lesion type is considered. The sample contribution of each lung function examination dimension can be evaluated more comprehensively by the weighted discrimination signal intensity obtained based on the feature response sensitivity and the discrimination contribution degree; the accuracy of classification model training is higher when the weighted discrimination signal intensity is used as the sample weight, and the accuracy of lung function examination data classification is improved.

The present application relates to the technical field of intelligent auxiliary diagnosis, in particular to a gastrointestinal early cancer intelligent auxiliary diagnosis system based on deep learning technology. It comprises: an image acquisition and processing unit acquires images in the gastrointestinal tract and performs preprocessing; a lesion detection unit uses Faster R-CNN to locate the lesion area, and at the same time, combines endoscope pose data to calculate local curvature to generate a rotation angle adaptive anchor box to optimize the process of locating the lesion area; a lesion segmentation unit segments the lesion area and outputs the segmentation mask of the lesion; a lesion classification unit classifies the segmented lesion area based on the segmented lesion area using a convolutional neural network model to classify and diagnose different lesion types; a diagnosis fusion unit combines the lesion detection, lesion classification results and lesion segmentation mask to output an auxiliary diagnosis report. The present application effectively deals with motion interference and imaging artifacts in endoscopic imaging through a multi-stage collaborative optimization strategy.

A method and apparatus for identifying fundus lesions are provided. The method includes: acquiring OCT fundus images of a specific patient; using a first neural network model to determine the boundary information of retinal tissue in the OCT fundus images, and segmenting the OCT fundus images into multiple image blocks based on the boundary information of the retinal tissue; providing the multiple image blocks to a second neural network model to obtain the fundus lesion type of the specific patient; the second neural network model calculating the feature vector of each image block and the attention weight of each image block for the specified fundus lesion type, and obtaining the fused feature vector through weighted calculation to obtain the identification result for the specified fundus lesion type. The second neural network model combines spatial saliency and scanning position saliency to enhance important information in the image block group and filter out unimportant information, thereby achieving low-cost model training and application.

The invention discloses a gastrointestinal tract lesion detection and classification method and diagnosis system based on deep learning mixed stacking integration, and the method comprises the steps: obtaining an internal image of a gastrointestinal tract through an endoscope image collection system, inputting the image into a trained deep learning mixed stacking integration model, and obtaining classification labels and detection results of a plurality of lesion types; the model is combined with various convolutional neural networks and a machine learning classifier, high-precision recognition and classification of various lesions such as polyps, inflammations, ulcers and bleeding in gastrointestinal tract images are achieved in a feature fusion and integrated learning mode, and a significance map of a focus area is output through an interpretability analysis method. According to the method, the accuracy and efficiency of gastrointestinal tract lesion detection and classification can be greatly improved, and misdiagnosis and missed diagnosis risks caused by traditional subjective judgment depending on doctors are reduced; and meanwhile, additional hardware investment is not needed, and image analysis and lesion diagnosis can be completed only by utilizing an existing endoscopic image and a computer software algorithm.

The application discloses a kind of based on multi-lead time-frequency collaborative hybrid myocardial infarction classification and positioning method, comprising the following steps: A: obtaining the 12-lead electrocardiogram signal of patient;B: 12-lead electrocardiogram signal is respectively preprocessed;C: construct the myocardial infarction classification and positioning model based on multi-lead time-frequency collaborative hybrid attention network, and utilize different myocardial infarction lesion type electrocardiogram data to train model, obtain the myocardial infarction classification and positioning model after training;D: based on the preprocessed 12-lead electrocardiogram signal to be classified and positioned, utilize the myocardial infarction classification and positioning model after training to classify and position, obtain corresponding classification and positioning result.The application can improve the accuracy and robustness of myocardial infarction classification and positioning, provide auxiliary data support for doctor to carry out diagnosis result verification.

The invention discloses a feature classification method and system based on an alimentary canal internal image, and relates to the technical field of image recognition, and the method comprises the steps: collecting an alimentary canal image in real time through an endoscope, and carrying out the preprocessing; based on a position estimation network formed by a convolutional neural network and a time sequence encoder, the image sequence is mapped to a standardized alimentary canal template, and endoscope position evaluation is achieved; the attention weight of each sample is calculated according to the time sequence similarity of the similar positions, and individual adaptive optimization of the image recognition model is completed; outputting an identification result, severity and confidence distribution of a focus type through an image identification model, constructing a graph structure model taking the focus type as a node, performing information propagation in a graph structure, and deducing focus probability distribution of an undetected area; and an inference result is fed back to the attention weight of the classification output layer, so that dynamic self-learning and global optimization of the model are realized. According to the method, the accuracy and stability of alimentary canal image recognition are remarkably improved.

The invention discloses a vocal cord disease classification method and device based on collaborative optimization of U-Net and a multi-scale attention classification network. The method comprises the following steps: (1) preprocessing a laryngoscope image; (2) constructing an improved U-Net segmentation network model; (3) constructing a segmentation-classification collaborative optimization network model; (4) designing a dual-task joint loss function; and (5) generating an interpretable diagnosis report. According to the vocal cord disease classification method based on collaborative optimization of the U-Net and the multi-scale attention classification network, through data preprocessing, network structure design optimization, segmentation and classification dual-task collaborative architecture construction and dual-task loss function joint optimization, the segmentation precision and the classification accuracy are remarkably improved, and the classification accuracy is improved. A more reliable diagnosis basis is provided for clinicians, lesion types such as vocal cord polyps, cysts and nodules are rapidly distinguished in an auxiliary mode, and the early screening process of vocal cord diseases is remarkably optimized.

The utility model belongs to the technical field of medical teaching appliances, and particularly relates to an aortic dissection inflation model for teaching, which comprises a base, a transparent plate is arranged on the base, and a simulation inner membrane is arranged on one side of the transparent plate. A first air bag, a second air bag and a third air bag which are sequentially connected and mutually spaced are arranged on the surface of the simulation inner film, the simulation inner film, the first air bag, the second air bag and the third air bag are all of a half-section structure, and an air supply device is arranged in the base; the first air bag, the second air bag and the third air bag can be selectively inflated through the air supply device arranged in the base, so that various lesion types of the aortic dissection are simulated. The method enables the teaching process to be more vivid, and helps students to form deeper memory. Meanwhile, the simulated inner membrane and each air bag adopt a half-section structure design and are matched with a transparent plate, so that students can intuitively observe the internal structure of the inner membrane, and the teaching activity is more vivid and vivid.

The invention discloses an AI-guided pancreaticobiliary accurate intubation auxiliary system in an ERCP operation, and relates to the technical field of pancreaticobiliary accurate intubation assistion.The system specifically comprises a multi-dimensional pancreaticobiliary data acquisition module, a multi-center patient data source and range are determined through the multi-dimensional pancreaticobiliary data acquisition module, multi-type data are collected in a standard mode, standardized basic data are formed through preprocessing, and the standardized basic data are stored in a database; the pancreaticobiliary duct feature fusion module is used for hierarchically constructing a data set on the basis of anatomical variation and lesion types, extracting and standardizing four types of features, and weighting and fusing the four types of features into uniform dimension feature vectors through an attention mechanism network to solve the problem of data isomerism; according to the method, a multi-task loss balance mechanism and model distillation technology optimization are utilized to adapt to intraoperative real-time calculation, model suitability is verified through multi-center clinical tests, intubation success rate, operation time consumption and complication rate indexes before and after assistance are compared, and finally ERCP operation quality and safety are promoted in an assisted mode.

The invention discloses a focus identification method and device based on a magnetic control capsule endoscopy image, and relates to the technical field of medical detection assistance, and the method comprises the following steps: collecting the magnetic control capsule endoscopy image, obtaining the movement position and speed of a capsule in real time, and screening out the endoscopy image in a high-speed movement time period as a target image set; reading gradient change data of the target image based on a Sobel algorithm, generating a saliency value, and obtaining a motion blur score through global average pooling to represent image definition; images which do not meet the definition requirement are screened out according to the scores, an image reconstruction device is constructed, the learning rate is dynamically adjusted, the image definition is enhanced, and an identification endoscope image is obtained; inputting the recognition endoscope image and the remaining image into the trained focus recognition model, and outputting an image containing a focus type and a region mark; the quality of the image finally input into the lesion analysis model is ensured, and the lesion identification accuracy is remarkably improved.

The invention provides a deep learning-based scalp health condition detection method and system, and relates to the technical field of image data processing, and the method comprises the steps: obtaining dermatoscope images and optical coherence tomography data of different scalp regions; constructing a voxel model with epidermis morphological characteristics and subcutaneous tissue scattering characteristics according to optical coherence tomography data; performing multi-layer convolution and pooling operation on the voxel model by using a pre-trained convolutional neural network to extract a high-dimensional feature vector; mapping the high-dimensional feature vector to a low-dimensional space by using a manifold learning algorithm, and calculating a target distance; on the basis of the target distance, the scalp area where the tissue type is the scar lesion type is marked as the irreversible alopecia area, so that detection of the scalp health condition is completed, subcutaneous hair follicle structure disappearance and fibrosis scar tissue are accurately distinguished under the non-invasive condition, and the detection accuracy is improved. The accuracy of scar alopecia judgment and the reliability of irreversible alopecia area recognition are improved.