Medical graphic image artificial intelligence modeling and recognition system

Abstract

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.

Description

Technical Field

[0001] The present invention relates to the field of intelligent modeling and recognition technology, and more specifically, to a medical graphics and imaging artificial intelligence modeling and recognition system. Background Art

[0002] With the rapid development of science and technology, artificial intelligence (AI) technology has gradually penetrated into various industries, among which the medical industry is one of the areas that has benefited the most; the application of AI in the medical field, especially in medical imaging diagnosis, has shown great potential and value.

[0003] Medical imaging diagnosis is an indispensable part of clinical medicine. It is of great significance for early detection of diseases, accurate diagnosis and selection of treatment plans. Although a number of studies and commercial applications have achieved certain results in the field of medical imaging, there are still some limitations, such as data diversity, complexity and model interpretability. This requires the construction of a medical image recognition system that integrates advanced AI technology to achieve more efficient and accurate lesion identification and diagnosis.

[0004] However, traditional manual analysis still has some shortcomings in actual use. For example, traditional analysis methods only segment the image once to determine the lesion area. This single segmentation method will lead to increased errors and increased probability of misdiagnosis, especially in the case of processing complex images. The lesion area may be partially obscured or have similar tissue structures. Due to the lack of a secondary verification mechanism, traditional methods cannot adapt to slight changes in the image, further reducing the accuracy of diagnosis. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a medical graphic image artificial intelligence modeling and recognition system to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] Data acquisition module: used to obtain the patient's CT scan medical image data, and then quickly transmit the CT image data from the hospital information system to the image data preprocessing module through a high-speed data transmission interface;

[0008] Image data preprocessing module: performs noise removal and image enhancement preprocessing on the input medical image;

[0009] Image segmentation module: used for performing image segmentation on the medical image after image processing, dividing the segmented medical image into a lesion area to be determined and a normal area, and transmitting the lesion area to be determined in the medical image to the feature extraction module;

[0010] Feature extraction module: It uses deep learning algorithms to extract key features from medical images in the lesion area to be determined, calculates the lesion value based on the key features, and divides the lesion area to be determined into a normal area and a lesion area;

[0011] Active learning model building module: used to input the features and images of the lesion area into the active learning model and conduct training;

[0012] Recognition and diagnosis module: The medical image to be identified is input into the trained active learning model, and the model outputs the recognition result.

[0013] Preferably, in the data acquisition module, the data acquisition module is connected to the hospital's information system to automatically collect and update the patient's latest imaging data; wherein the image data input format supports DICOM, JPEG, and PNG medical imaging standard formats.

[0014] Preferably, in the image data preprocessing module, noise removal uses Gaussian filtering to eliminate random noise in the image data; and the image data after noise removal is subjected to histogram equalization to enhance image details.

[0015] Preferably, in the image segmentation module, the image segmentation method is specifically:

[0016] Step 1: Standardize the medical image and adjust the size of the medical image to a uniform size;

[0017] Step 2: Select the number of clusters to be segmented and randomly initialize the fuzzy membership matrix. Each column of the membership matrix represents a cluster and the sum of each row is 1, that is: ,in, It is represented as the membership of the a-th pixel in the medical image to the b-th cluster, and c is the number of clusters;

[0018] Step 3: Calculate the cluster center. Calculate the center of each cluster based on the membership matrix. The calculation method of the center value of each cluster is as follows:

[0019] ,in, is represented by the center value of each cluster, D is represented by the total number of pixels in the image, It is represented as the feature vector of the ath pixel; d is represented as the fuzzy factor, which takes a value between 1 and 2;

[0020] Step 4: Update the membership matrix according to the current cluster center. The updated calculation method is as follows:

[0021] ,in, It is represented as the updated membership matrix, c is the number of clusters, f is the center of the fth cluster, and h is the fuzzy factor, which takes a value between 1 and 2. It is expressed as the distance between the ath pixel and the bth cluster center, It is expressed as the distance between the ath pixel and the fth cluster center;

[0022] Step 5: Check the convergence of the algorithm. Determine whether the algorithm converges by calculating the change in the membership matrix. The specific calculation method for the change in the membership matrix is:

[0023] ,in, Expressed as the change in the membership matrix, Represented as the fuzzy membership matrix calculated after the current iteration; It is represented by the fuzzy membership matrix calculated in the previous iteration step. That is, the membership value of the previous round;

[0024] Step 6: Generate segmentation results based on the fuzzy membership matrix and use the binary method for segmentation. The specific calculation method is:

[0025] ,in, Represented as the result of binary segmentation, It is represented as the membership of the a-th pixel to the b-th cluster in the medical image.

[0026] Preferably, in the feature extraction module, the deep learning algorithm selects a convolutional neural network, and the convolutional neural network is used to extract the grayscale distribution value, element density value, and texture value in the lesion area to be determined.

[0027] Preferably, in the model training module, the method for constructing the active learning model is specifically:

[0028] The selected sample set T first defines a sampling strategy , this strategy calculates the probability of each sample in the sample set T being selected; then, the labeled sample set U is used to train the model to obtain a classifier w; this classifier w can predict the category of unlabeled samples, and according to the previously defined sampling strategy, the most valuable samples are selected from the prediction results for labeling; in this process, the number of labeled samples will be dynamically adjusted according to the sampling strategy; in each iteration, the most valuable z samples are selected for labeling, where the specific value of z will be flexibly adjusted according to the sampling strategy and the overall size of the data set; this iterative process will continue until the preset stop condition is met.

[0029] Preferably, in the identification and diagnosis module, the image of the lesion area is input into the active learning model again for identification, and the model outputs the disease type, location and size in the lesion area, and generates a preliminary diagnosis report and suggestions;

[0030] In the output results of the active learning model, the identified lesion area is first visualized, and the location of the lesion is highlighted so that medical staff can intuitively understand the distribution of the lesions. After the preliminary diagnosis report and recommendations are generated, the active learning model does not stop working immediately; instead, it enters a feedback and optimization loop. The system compares this preliminary report with the preset high-precision medical diagnostic standards and evaluates the accuracy and completeness of the diagnosis through algorithms.

[0031] Technical effects and advantages of the present invention:

[0032] The present invention obtains CT image data from a hospital information system through a high-speed data transmission interface, and automatically collects and updates the latest image data of patients; then, the input medical image is subjected to noise removal and image enhancement preprocessing, Gaussian filtering is used to eliminate random noise, and histogram equalization is performed to enhance image details; then, the image is segmented into a lesion area to be determined and a normal area, and the lesion area to be determined in the medical image is transmitted to a feature extraction module. In the feature extraction module, a convolutional neural network is used to extract key features, and a lesion value is obtained based on the calculation of the key features, and the lesion area to be determined is divided into a normal area and a lesion area; finally, the features and images of the lesion area are input into an active learning model to be constructed and trained, and the most valuable samples are selected through an iterative process for annotation until a preset stop condition is met; finally, the medical image to be identified is input into a trained active learning model, and the model outputs the identification result, and generates a preliminary diagnosis report and suggestion. The present invention calculates the lesion value based on the extracted features after image segmentation, and distinguishes the lesion area to be determined again based on the lesion value. Through the mechanism of secondary verification, it adapts to slight changes in the image, reduces the error and misdiagnosis probability, and improves the accuracy of diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of module connection of the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] See also Figure 1 As shown, the present invention provides a medical graphic image artificial intelligence modeling and recognition system, which includes a data acquisition 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.

[0036] Data acquisition module: used to obtain the patient's CT scan medical image data, and then quickly transmit the CT image data from the hospital information system to the image data preprocessing module through a high-speed data transmission interface;

[0037] In the data acquisition module, the data acquisition module is connected to the hospital's information system to automatically collect and update the patient's latest imaging data; wherein, the image data input format supports DICOM, JPEG, and PNG medical imaging standard formats.

[0038] Image data preprocessing module: performs noise removal and image enhancement preprocessing on the input medical image;

[0039] In the image data preprocessing module, noise removal uses Gaussian filtering to eliminate random noise in the image data; the image data after noise removal is subjected to histogram equalization to enhance image details;

[0040] The specific method of using Gaussian filtering to eliminate random noise in image data is:

[0041] Step 1: Create a Gaussian filter based on the determined parameters; calculate a Gaussian weight matrix, each element of which represents the pixel weight at the corresponding position.

[0042] Step 2: Scan each pixel in the image with the created Gaussian filter. For each pixel, replace the value of the pixel with the weighted average grayscale value of the pixels in the neighborhood determined by the filter.

[0043] Step 3: Repeat the above steps until all pixels in the image have been processed to obtain a denoised image.

[0044] Image segmentation module: used for performing image segmentation on the medical image after image processing, dividing the segmented medical image into a lesion area to be determined and a normal area, and transmitting the lesion area to be determined in the medical image to the feature extraction module;

[0045] In the image segmentation module, the image segmentation method is specifically as follows:

[0046] Step 1: Standardize the medical image and adjust the size of the medical image to a uniform size;

[0047] Step 2: Select the number of clusters to be segmented and randomly initialize the fuzzy membership matrix. Each column of the membership matrix represents a cluster and the sum of each row is 1, that is: ,in, It is represented as the membership of the a-th pixel in the medical image to the b-th cluster, and c is the number of clusters;

[0048] Step 3: Calculate the cluster center. Calculate the center of each cluster based on the membership matrix. The calculation method of the center value of each cluster is as follows:

[0049] ,in, is represented by the center value of each cluster, D is represented by the total number of pixels in the image, It is represented as the feature vector of the ath pixel; d is represented as the fuzzy factor, which takes a value between 1 and 2;

[0050] Step 4: Update the membership matrix according to the current cluster center. The updated calculation method is as follows:

[0051] ,in, It is represented as the updated membership matrix, c is the number of clusters, f is the center of the fth cluster, and h is the fuzzy factor, which takes a value between 1 and 2. It is expressed as the distance between the ath pixel and the bth cluster center;

[0052] Step 5: Check the convergence of the algorithm. Determine whether the algorithm converges by calculating the change in the membership matrix. The specific calculation method for the change in the membership matrix is:

[0053] ,in, Expressed as the change in the membership matrix, Represented as the fuzzy membership matrix calculated after the current iteration; It is represented by the fuzzy membership matrix calculated in the previous iteration step. That is, the membership value of the previous round;

[0054] The calculated change amount of the membership matrix is ​​compared with the preset change amount threshold. If the calculated change amount of the membership matrix is ​​less than the preset change amount threshold, a convergence instruction is output and the iteration is stopped; if the calculated change amount of the membership matrix is ​​greater than the preset change amount threshold, a non-convergence instruction is output and the process returns to step 3.

[0055] Step 6: Generate segmentation results based on the fuzzy membership matrix and use the binary method for segmentation. The specific calculation method is:

[0056] ,in, Represented as the result of binary segmentation, It is expressed as the membership of the a-th pixel to the b-th cluster in the medical image;

[0057] When the membership degree is calculated If the calculated membership degree is greater than or equal to the preset threshold, then this area is the lesion area to be determined; If it is smaller than the preset threshold, then this area is a normal area;

[0058] According to step 1 to step 6, the medical image is divided into a lesion area to be determined and a normal area.

[0059] Feature extraction module: It uses deep learning algorithms to extract key features from medical images in the lesion area to be determined, calculates the lesion value based on the key features, and divides the lesion area to be determined into a normal area and a lesion area;

[0060] In the feature extraction module, the deep learning algorithm selects a convolutional neural network, and the convolutional neural network is used to extract the grayscale distribution value, element density value, and texture value in the lesion area to be determined;

[0061] The calculation method of the grayscale distribution value is as follows:

[0062] , where H represents the grayscale distribution value in the image of the lesion area to be determined, Represented as the image of the lesion area to be determined Gray value of the position; It is represented as the position of the center of mass on the x-axis, It is expressed as the position of the center of mass on the y-axis. qj and qk are combined to form the center of mass ; It is represented as the sum of all gray values ​​in the image of the lesion area to be determined;

[0063] The method for calculating the position of the center of mass on the x-axis is as follows:

[0064] ,in, It is represented as the position of the center of mass on the x-axis, Indicated as the area of ​​lesion to be determined Gray value of the position;

[0065] The calculation method of the center of mass position on the y-axis is as follows:

[0066] ,in, Expressed as the position of the center of mass on the y-axis, Indicated as the area of ​​lesion to be determined Gray value of the position;

[0067] The specific method for calculating the element density value in the image of the lesion area to be determined is:

[0068] , where L represents the element density value in the image of the lesion area to be determined, Expressed as the linear attenuation coefficient of tissue to X-rays, Expressed as the linear attenuation coefficient of water for X-rays;

[0069] Attenuation coefficient of water It is set as the reference standard. The densest tissue in the body has the greatest attenuation of X-ray absorption, so its element density value is the highest and is set at +1000Hu.

[0070] Air has the lowest density and the smallest absorption attenuation of X-rays, so its CT value is the lowest, which is set at -1000Hu.

[0071] The density of other tissues in the human body is between that of air and compact bone, so their elemental density values ​​lie between -1000Hu and +1000Hu;

[0072] The specific method for calculating the texture value in the image of the lesion area to be determined is:

[0073] , where M represents the texture value in the image of the lesion area to be determined, N represents the contrast, P represents the correlation, and Q represents the entropy. , , Expressed as weight coefficient;

[0074] The specific calculation method of contrast is:

[0075] , where N represents the contrast, It is represented as the frequency of pixel pairs in the gray-level co-occurrence matrix, and m and n are represented as the gray values ​​of two pixels;

[0076] The calculation method of correlation is as follows:

[0077] , where P represents the correlation, It is represented as the frequency of pixel pairs in the gray-level co-occurrence matrix, m and n are represented as the gray values ​​of two pixels, is represented as the mean of the rows, is represented as the mean of the column, Expressed as the standard deviation of the row, Expressed as the standard deviation of the column;

[0078] , where Q represents entropy, It is expressed as the frequency of pixel pairs in the gray-level co-occurrence matrix;

[0079] Lesion area identification module: used to calculate the lesion value based on the extracted moment of inertia value, element density value and texture value, and determine the lesion area according to the lesion value;

[0080] In the lesion area identification module, the calculation method of the lesion value is specifically as follows:

[0081] , where Z represents the lesion value, H represents the moment of inertia value, L represents the element density value in the image of the lesion area to be determined, and M represents the texture value in the image of the lesion area to be determined;

[0082] If the calculated lesion value is greater than the preset lesion value, a lesion instruction is output, and the lesion area to be determined is a lesion area; if the calculated lesion value is less than the preset lesion value, a non-lesion instruction is output, and the lesion area to be determined is a normal area;

[0083] The features and images of the lesion area are transmitted to the active learning model training module for active learning.

[0084] Active learning model building module: used to input the features and images of the lesion area into the active learning model and conduct training;

[0085] In the active learning model construction module, the method for constructing the active learning model is specifically as follows:

[0086] The selected sample set T first defines a sampling strategy , this strategy calculates the probability of each sample in the sample set T being selected; then, the labeled sample set U is used to train the model to obtain a classifier w; this classifier w can predict the category of unlabeled samples, and select the most valuable samples from the prediction results for labeling according to the previously defined sampling strategy; in this process, the number of labeled samples will be dynamically adjusted according to the sampling strategy; in each iteration, the most valuable z samples are selected for labeling, where the specific value of z will be flexibly adjusted according to the sampling strategy and the overall size of the data set; this iterative process will continue until the preset stop condition is met;

[0087] Among them, the calculation method in the active learning model is specifically as follows:

[0088] ,in, It means selecting the sample with the highest score in the unlabeled sample set, J represents the unlabeled sample, and w represents the classifier; Expressed as a classifier performance metric;

[0089] Subject to , where U represents the set of labeled samples, It is expressed as the upper limit of the number of labeled samples;

[0090] According to the sampling strategy, the most valuable samples are selected for annotation. The specific calculation method is as follows:

[0091] , where T represents the labeled sample. It is represented as a sampling strategy;

[0092] The calculation method of the final selected labeled sample set is as follows:

[0093] ,in, Represents the final selected set of labeled samples, Expressed as a classifier performance metric;

[0094] Active learning is a continuous cycle until the stopping criterion is reached.

[0095] Recognition and diagnosis module: The medical image to be recognized is input into the trained active learning model, and the model outputs the recognition result;

[0096] In the recognition and diagnosis module, the image of the lesion area is input into the active learning model again for recognition, and the model outputs the disease type, location and size in the lesion area, and generates a preliminary diagnosis report and suggestions;

[0097] In the output results of the active learning model, the identified lesion area is first visualized, and the location of the lesion is highlighted so that medical staff can intuitively understand the distribution of the lesions. After the preliminary diagnosis report and recommendations are generated, the active learning model does not stop working immediately; instead, it enters a feedback and optimization loop. The system compares this preliminary report with the preset high-precision medical diagnostic standards and evaluates the accuracy and completeness of the diagnosis through algorithms.

[0098] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A medical image artificial intelligence modeling and recognition system, characterized in that: include: Data acquisition module: used to obtain the patient's CT scan medical image data, and then quickly transmit the CT image data from the hospital information system to the image data preprocessing module through a high-speed data transmission interface; Image data preprocessing module: performs noise removal and image enhancement preprocessing on the input medical image; Image segmentation module: used for performing image segmentation on the medical image after image processing, dividing the segmented medical image into a lesion area to be determined and a normal area, and transmitting the lesion area to be determined in the medical image to the feature extraction module; Feature extraction module: It uses deep learning algorithms to extract key features from medical images in the lesion area to be determined, calculates the lesion value based on the key features, and divides the lesion area to be determined into a normal area and a lesion area; The specific method for calculating the element density value in the image of the lesion area to be determined is: , where L represents the element density value in the image of the lesion area to be determined, Expressed as the linear attenuation coefficient of tissue to X-rays, Expressed as the linear attenuation coefficient of water for X-rays; The specific method for calculating the texture value in the image of the lesion area to be determined is: , where M represents the texture value in the image of the lesion area to be determined, N represents the contrast, P represents the correlation, and Q represents the entropy; Active learning model building module: used to input the features and images of the lesion area into the active learning model and conduct training; Recognition and diagnosis module: The medical image to be identified is input into the trained active learning model, and the model outputs the recognition result.

2. The medical image artificial intelligence modeling and recognition system according to claim 1, characterized in that: In the data acquisition module, the data acquisition module is connected to the hospital's information system to automatically collect and update the patient's latest imaging data; wherein, the image data input format supports DICOM, JPEG, and PNG medical imaging standard formats.

3. The medical image artificial intelligence modeling and recognition system according to claim 1, characterized in that: In the image data preprocessing module, noise removal uses Gaussian filtering to eliminate random noise in image data; and histogram equalization is performed on the image data after noise removal to enhance image details.

4. The medical image artificial intelligence modeling and recognition system according to claim 1, characterized in that: In the image segmentation module, the image segmentation method is specifically as follows: Step 1: Standardize the medical image and adjust the size of the medical image to a uniform size; Step 2: Select the number of clusters to be segmented and randomly initialize the fuzzy membership matrix. Each column of the membership matrix represents a cluster and the sum of each row is 1, that is: ,in, It is represented as the membership of the a-th pixel in the medical image to the b-th cluster, and c is the number of clusters; Step 3: Calculate the cluster center. Calculate the center of each cluster based on the membership matrix. The calculation method of the center value of each cluster is as follows: ,in, is represented by the center value of each cluster, D is represented by the total number of pixels in the image, It is represented as the feature vector of the ath pixel; d is represented as the fuzzy factor, which takes a value between 1 and 2; Step 4: Update the membership matrix according to the current cluster center. The updated calculation method is as follows: ,in, It is represented as the updated membership matrix, c is the number of clusters, f is the center of the fth cluster, and h is the fuzzy factor, which takes a value between 1 and 2. It is expressed as the distance between the ath pixel and the bth cluster center, It is expressed as the distance between the ath pixel and the fth cluster center; Step 5: Check the convergence of the algorithm. Determine whether the algorithm converges by calculating the change in the membership matrix. The specific calculation method for the change in the membership matrix is: ,in, Expressed as the change in the membership matrix, Represented as the fuzzy membership matrix calculated after the current iteration; Represents the fuzzy membership matrix calculated in the previous iteration step; that is, the membership value of the previous round; Step 6: Generate segmentation results based on the fuzzy membership matrix and use the binary method for segmentation. The specific calculation method is: ,in, Represented as the result of binary segmentation, It is expressed as the membership of the a-th pixel to the b-th cluster in the medical image; When the membership degree is calculated If the calculated membership degree is greater than or equal to the preset threshold, then this area is the lesion area to be determined; If it is smaller than the preset threshold, the area is a normal area.

5. The medical image artificial intelligence modeling and recognition system according to claim 1, characterized in that: In the feature extraction module, the deep learning algorithm selects a convolutional neural network, and the moment of inertia value, element density value, and texture value in the lesion area to be determined are extracted through the convolutional neural network; The calculation method of the grayscale distribution value is as follows: , where H represents the grayscale distribution value in the image of the lesion area to be determined, Represented as the image of the lesion area to be determined Gray value of the position; It is represented as the position of the center of mass on the x-axis, It is expressed as the position of the center of mass on the y-axis. qj and qk are combined to form the center of mass ; It is represented as the sum of all gray values ​​in the image of the lesion area to be determined.

6. The medical image artificial intelligence modeling and recognition system according to claim 5, characterized in that: The method for calculating the position of the center of mass on the x-axis is as follows: ,in, It is represented as the position of the center of mass on the x-axis, Indicated as the area of ​​lesion to be determined Gray value of the position; The calculation method of the center of mass position on the y-axis is as follows: ,in, It is represented as the position of the center of mass on the y-axis, Indicated as the area of ​​lesion to be determined Grayscale value of the position.

7. The medical image artificial intelligence modeling and recognition system according to claim 1, characterized in that: In the active learning model construction module, the method for constructing the active learning model is specifically as follows: The selected sample set T first defines a sampling strategy , this strategy calculates the probability of each sample in the sample set T being selected; then, the labeled sample set U is used to train the model to obtain a classifier w; this classifier w can predict the category of unlabeled samples, and select the most valuable samples from the prediction results for labeling according to the previously defined sampling strategy; in this process, the number of labeled samples will be dynamically adjusted according to the sampling strategy; in each iteration, the most valuable z samples are selected for labeling, where the specific value of z will be flexibly adjusted according to the sampling strategy and the overall size of the data set; this iterative process will continue until the preset stop condition is met; Among them, the calculation method in the active learning model is specifically as follows: ,in, It means selecting the sample with the highest score in the unlabeled sample set, J represents the unlabeled sample, and w represents the classifier; Expressed as a classifier performance metric; Subject to , where U represents the set of labeled samples, It is expressed as the upper limit of the number of labeled samples; According to the sampling strategy, the most valuable samples are selected for annotation. The specific calculation method is as follows: , where T represents the labeled sample. It is represented as a sampling strategy; The calculation method of the final selected labeled sample set is as follows: ,in, Represents the final selected set of labeled samples, It is expressed as a classifier performance metric.

8. The medical image artificial intelligence modeling and recognition system according to claim 1, characterized in that: In the recognition and diagnosis module, the image of the lesion area is input into the active learning model again for recognition. The model outputs the disease type, location and size in the lesion area, and generates a preliminary diagnosis report and suggestions.

Images