Breast cancer benign and malignant intelligent analysis system based on artificial intelligence

Abstract

The invention discloses a breast cancer benign and malignant intelligent analysis system based on artificial intelligence, and belongs to the technical field of breast cancer, and the system comprises a data collection module which is used for collecting breast cancer historical data; the data processing module is used for processing the historical breast cancer data; the model training module is used for constructing a breast cancer benign and malignant intelligent analysis model and determining an optimal breast cancer benign and malignant intelligent analysis model; the intelligent analysis module is used for intelligently analyzing the breast cancer benign and malignant conditions of the patient and determining an intelligent analysis result of the breast cancer benign and malignant conditions of the patient; and the output management module is used for outputting the benign and malignant intelligent analysis result of the breast cancer of the patient. The method solves the problem of poor breast cancer benign and malignant intelligent analysis effect caused by incapability of intelligently analyzing and effectively identifying and classifying breast cancer benign and malignant breast cancer in the prior art. The method can intelligently analyze and effectively identify and classify the benign and malignant breast cancer, and can improve the intelligent analysis effect of the benign and malignant breast cancer.

Description

Technical Field

[0001] The present invention relates to the technical field of breast cancer, and in particular to an intelligent analysis system for benign and malignant breast cancer based on artificial intelligence. Background Art

[0002] With the development of science and technology, the application of artificial intelligence in the medical field has become more and more extensive. In recent years, breast cancer has become one of the most common malignant tumors in women, and its early diagnosis and treatment have an important impact on the patient's prognosis.

[0003] Existing technologies, such as manual diagnosis and X-ray methods, cannot perform intelligent analysis and effectively identify and classify benign and malignant breast cancer, resulting in poor results in intelligent analysis of benign and malignant breast cancer. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent analysis system for benign and malignant breast cancer based on artificial intelligence, which can perform intelligent analysis and effectively identify and classify benign and malignant breast cancer, improve the effect of intelligent analysis of benign and malignant breast cancer, and solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: The AI-based intelligent analysis system for benign and malignant breast cancer includes: The data collection module is used to collect the patient's personal medical history data, medical imaging data, pathological tissue data and genomics data to determine the AI-based breast cancer historical data; A data processing module, used to process artificial intelligence-based breast cancer historical data and determine artificial intelligence-based breast cancer characteristic data; The model training module is used to construct an AI-based intelligent analysis model for benign and malignant breast cancer, and to test and optimize the AI-based intelligent analysis model for benign and malignant breast cancer to determine the optimal intelligent analysis model for benign and malignant breast cancer; Intelligent analysis module, used to intelligently analyze the benign and malignant conditions of the patient's breast cancer and determine the intelligent analysis results of the patient's benign and malignant breast cancer; The output management module is used to output the intelligent analysis results of the benign and malignant status of the patient's breast cancer, and to display the intelligent analysis report of the patient's breast cancer in a visual form, and to perform intelligent treatment management on the patient's breast cancer.

[0006] Preferably, the data collection module comprises: The personal medical history collection unit is used to collect the patient's age, gender, family medical history, past medical history, menstrual status and reproductive history to obtain personal medical history data; A medical image collection unit, used to collect breast X-rays, ultrasound images and magnetic resonance imaging of patients to obtain medical image data; Pathological tissue collection unit, used to collect the patient's pathological sections and immunohistochemistry to obtain pathological tissue data; A genomics collection unit is used to collect the patient's gene expression profile, gene mutation and amplified genetic information to obtain genomics data; Among them, artificial intelligence-based breast cancer historical data is determined based on personal medical history data, medical imaging data, pathological tissue data and genomics data.

[0007] Preferably, the data processing module includes: A data cleaning unit, used to clean historical breast cancer data based on artificial intelligence; Based on the data cleaning tool, duplicate values, missing values ​​and outliers in the AI-based breast cancer historical data are identified and processed; When duplicate values ​​exist in AI-based breast cancer historical data, the duplicate values ​​are removed; When missing values ​​exist in AI-based breast cancer historical data, samples containing missing values ​​are deleted or the median is used to fill the missing values; When outliers exist in AI-based breast cancer historical data, samples containing outliers are deleted or the average value is used to replace the outliers.

[0008] Preferably, the data processing module further includes: A data conversion unit for converting historical breast cancer data based on artificial intelligence; Perform format and type conversion on AI-based breast cancer historical data, remove dimensional differences between AI-based breast cancer historical data, and determine standardized breast cancer historical data; A feature extraction unit, used for extracting features from standardized breast cancer historical data; The random forest algorithm based on feature importance evaluation is used to evaluate the importance of the extracted features, evaluate the influence of the extracted features on the classification of benign and malignant breast cancer, select the features that can best distinguish benign and malignant breast cancer lesions, and determine the breast cancer feature data based on artificial intelligence.

[0009] Preferably, the model training module includes: A data division unit, used for dividing the artificial intelligence-based breast cancer feature data into a training set and a test set; A model building unit, used to build an AI-based intelligent analysis model for benign and malignant breast cancer; Based on deep learning technology, the training set and the benign and malignant breast cancer labels are input into the deep learning model. Through continuous training iterations, the deep learning model can autonomously learn the benign and malignant classification of breast cancer and determine the benign and malignant breast cancer intelligent analysis model based on artificial intelligence. The test set is input into the artificial intelligence-based intelligent analysis model of benign and malignant breast cancer, and the performance of the artificial intelligence-based intelligent analysis model of benign and malignant breast cancer is tested based on the test set to determine whether the artificial intelligence-based intelligent analysis model of benign and malignant breast cancer can achieve the benign and malignant breast cancer intelligent analysis classification effect.

[0010] Preferably, judging whether the breast cancer benign and malignant intelligent analysis model based on artificial intelligence can achieve the effect of intelligent analysis and classification of breast cancer benign and malignant includes: Obtain the results of the performance test of the AI-based intelligent analysis model for benign and malignant breast cancer using the test set, and determine whether the AI-based intelligent analysis model for benign and malignant breast cancer can achieve the effect of intelligent analysis and classification of benign and malignant breast cancer based on the precision and recall rate of the AI-based intelligent analysis model for benign and malignant breast cancer; When the artificial intelligence-based intelligent analysis model for benign and malignant breast cancer cannot achieve the benign and malignant intelligent analysis classification effect of breast cancer, the parameters and structure of the artificial intelligence-based intelligent analysis model for benign and malignant breast cancer are continuously adjusted, the artificial intelligence-based intelligent analysis model for benign and malignant breast cancer is continuously iteratively optimized, and the performance of the artificial intelligence-based intelligent analysis model for benign and malignant breast cancer is verified based on the cross-validation method to achieve the best benign and malignant breast cancer classification effect and determine the optimal intelligent analysis model for benign and malignant breast cancer.

[0011] Preferably, the intelligent analysis module includes: A model deployment unit, used to deploy the optimal breast cancer benign and malignant intelligent analysis model in an actual breast cancer benign and malignant intelligent analysis environment; Intelligent analysis unit, used to intelligently analyze the benign and malignant status of the patient's breast cancer; The patient's real-time breast cancer data is input into the optimal intelligent analysis model for benign and malignant breast cancer. Based on the optimal intelligent analysis model for benign and malignant breast cancer, the patient's real-time breast cancer data is intelligently analyzed, and the benign and malignant conditions of the patient's breast cancer are classified to determine the intelligent analysis result of the patient's benign and malignant breast cancer, wherein the intelligent analysis result of the patient's benign and malignant breast cancer is that the patient's breast cancer is benign or the patient's breast cancer is malignant.

[0012] Preferably, the output management module includes: The output display unit is used to output the intelligent analysis results of the benign and malignant nature of the patient's breast cancer, analyze the patient's real-time breast cancer data and the intelligent analysis results of the patient's breast cancer, generate an intelligent analysis report of the patient's breast cancer, and display the intelligent analysis report of the patient's breast cancer in a visual form, so that doctors and patients can more intuitively understand the benign and malignant nature of the patient's breast cancer; The analysis and management unit is used to intelligently manage the benign and malignant conditions of the patient's breast cancer. According to the intelligent analysis results of the patient's breast cancer, a breast cancer treatment plan is formulated for the patient, and intelligent treatment management of the patient's breast cancer is performed based on the breast cancer treatment plan.

[0013] Preferably, personal medical history data, medical imaging data, pathological tissue data and genomics data are preprocessed to obtain preprocessed data; Extracting tissue pathology feature vectors based on pre-processed medical imaging data and pathological tissue data, and extracting gene mutation molecular feature vectors based on pre-processed genomics data; The tissue pathology feature vector and the gene mutation molecular feature vector are fused to obtain the fused multimodal features of the patient; Determine the differential gene expression parameters of the patient based on the fusion multimodal features, and determine the cell transcription profile expression data based on the differential gene expression parameters; Determine the patient's candidate mutation genes based on cell transcription profile expression data, and determine the patient's cancer signature based on the candidate mutation genes; Determine the mRNA data and miRNA data of the patient under cancerous lesions according to the cancer label, and obtain the patient's pathological image features based on the pre-processed medical imaging data; Reconstruct the pathological image features with the mRNA data and miRNA data to obtain the pathological image feature matrix, mRNA expression matrix and miRNA expression matrix; Construct a correlation structure diagram based on the pathological image feature matrix, mRNA expression matrix, and miRNA expression matrix; According to the association structure diagram, the pre-processed personal medical history data, medical imaging data, pathological tissue data and genomics data are labeled and packaged to generate an integrated file; Generate AI-based historical breast cancer data from integrated files.

[0014] Preferably, after acquiring the medical imaging data, the method further includes: Get the pixel gray value of each pixel in the medical image data, and determine the image chaos of the medical image according to the pixel gray value:

[0015]

[0016] Where Q represents the image chaos of the medical image, M represents the number of regions into which the medical image is divided into equal-area regions, and i represents the i-th divided region. It is represented as the average pixel gray value of the i-th divided area, and ln is represented as the natural logarithm;

[0017] Determine the uniformity of pixel grayscale distribution of the medical image according to the image chaos of the medical image, and confirm whether the medical image needs to be contrast enhanced according to the uniformity of pixel grayscale distribution;

[0018] If so, the contrast enhancement of medical images is achieved through the following formula:

[0019]

[0020] in, Represented as the grayscale value of the output medical image, Expressed as a dynamic coefficient, Represented as the grayscale value of the input medical image, It is expressed as the average value of grayscale in the neighborhood of point (i, j);

[0021] The enhanced medical images are used as medical imaging reference data.

[0022] Compared with the prior art, the present invention has the following beneficial effects: The present invention determines artificial intelligence-based breast cancer historical data by collecting the patient's personal medical history data, medical imaging data, pathological tissue data and genomics data, determines artificial intelligence-based breast cancer feature data by processing the artificial intelligence-based breast cancer historical data, determines the optimal intelligent analysis model for benign and malignant breast cancer by constructing an artificial intelligence-based intelligent analysis model for benign and malignant breast cancer, tests and optimizes the artificial intelligence-based intelligent analysis model for benign and malignant breast cancer, performs intelligent analysis on the patient's real-time breast cancer data based on the optimal intelligent analysis model for benign and malignant breast cancer, classifies the patient's breast cancer, determines the intelligent analysis result for the patient's breast cancer, outputs the intelligent analysis result for the patient's breast cancer, displays the intelligent analysis report for the patient's breast cancer in a visual form, performs intelligent treatment management on the patient's breast cancer, can perform intelligent analysis on the benign and malignant breast cancer and effectively identify and classify it, and can improve the effect of intelligent analysis on the benign and malignant breast cancer. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 The module block diagram of the artificial intelligence-based intelligent analysis system for benign and malignant breast cancer of the present invention. DETAILED DESCRIPTION

[0024] 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.

[0025] In order to solve the existing problem that the intelligent analysis of benign and malignant breast cancer cannot be performed and the classification cannot be effectively identified, resulting in poor results of intelligent analysis of benign and malignant breast cancer, please refer to Figure 1 , this embodiment provides the following technical solutions: The artificial intelligence-based intelligent analysis system for benign and malignant breast cancer includes: a data collection module, a data processing module, a model training module, an intelligent analysis module and an output management module.

[0026] It should be noted that through the interactive communication between the data collection module, the data processing module, the model training module, the intelligent analysis module and the output management module, the benign and malignant nature of breast cancer can be intelligently analyzed and effectively identified and classified, which can improve the effect of intelligent analysis of benign and malignant nature of breast cancer.

[0027] Among them, the patient's personal medical history data, medical imaging data, pathological tissue data and genomics data are collected through the data collection module to determine the breast cancer history data based on artificial intelligence.

[0028] In this embodiment, as a preferred technical solution of the present invention, the data collection module includes: The personal medical history collection unit is used to collect the patient's age, gender, family medical history, past medical history, menstrual status and reproductive history to obtain personal medical history data; A medical image collection unit, used to collect breast X-rays, ultrasound images and magnetic resonance imaging of patients to obtain medical image data; Pathological tissue collection unit, used to collect the patient's pathological sections and immunohistochemistry to obtain pathological tissue data; A genomics collection unit is used to collect the patient's gene expression profile, gene mutation and amplified genetic information to obtain genomics data; Among them, artificial intelligence-based breast cancer historical data is determined based on personal medical history data, medical imaging data, pathological tissue data and genomics data.

[0029] Among them, the artificial intelligence-based breast cancer historical data is processed by the data processing module to determine the artificial intelligence-based breast cancer characteristic data.

[0030] In this embodiment, as a preferred technical solution of the present invention, the data processing module includes: A data cleaning unit, used to clean historical breast cancer data based on artificial intelligence; Based on the data cleaning tool, duplicate values, missing values ​​and outliers in the AI-based breast cancer historical data are identified and processed; When duplicate values ​​exist in AI-based breast cancer historical data, the duplicate values ​​are removed; When missing values ​​exist in AI-based breast cancer historical data, samples containing missing values ​​are deleted or the median is used to fill the missing values; When there are outliers in the AI-based breast cancer historical data, the samples containing the outliers are deleted, or the outliers are replaced with the average value; It should be noted that by cleaning the AI-based breast cancer historical data, the subsequent processing accuracy and speed of the AI-based breast cancer historical data can be improved.

[0031] A data conversion unit for converting historical breast cancer data based on artificial intelligence; Perform format and type conversion on AI-based breast cancer historical data, remove dimensional differences between AI-based breast cancer historical data, and determine standardized breast cancer historical data; A feature extraction unit, used for extracting features from standardized breast cancer historical data; The random forest algorithm based on feature importance evaluation is used to evaluate the importance of the extracted features, evaluate the influence of the extracted features on the classification of benign and malignant breast cancer, select the features that can best distinguish benign and malignant breast cancer lesions, and determine the breast cancer feature data based on artificial intelligence.

[0032] Among them, an artificial intelligence-based intelligent analysis model for benign and malignant breast cancer is constructed through the model training module, and the artificial intelligence-based intelligent analysis model for benign and malignant breast cancer is tested and optimized to determine the optimal intelligent analysis model for benign and malignant breast cancer.

[0033] In this embodiment, as a preferred technical solution of the present invention, the model training module includes: A data division unit, used for dividing the artificial intelligence-based breast cancer feature data into a training set and a test set; A model building unit, used to build an AI-based intelligent analysis model for benign and malignant breast cancer; Based on deep learning technology, the training set and the benign and malignant breast cancer labels are input into the deep learning model. Through continuous training iterations, the deep learning model can autonomously learn the benign and malignant classification of breast cancer and determine the benign and malignant breast cancer intelligent analysis model based on artificial intelligence. The test set is input into the artificial intelligence-based intelligent analysis model of benign and malignant breast cancer, and the performance of the artificial intelligence-based intelligent analysis model of benign and malignant breast cancer is tested based on the test set to determine whether the artificial intelligence-based intelligent analysis model of benign and malignant breast cancer can achieve the benign and malignant breast cancer intelligent analysis classification effect.

[0034] In this embodiment, as a preferred technical solution of the present invention, judging whether the breast cancer benign and malignant intelligent analysis model based on artificial intelligence can achieve the effect of breast cancer benign and malignant intelligent analysis classification includes: Obtain the results of the performance test of the AI-based intelligent analysis model for benign and malignant breast cancer using the test set, and determine whether the AI-based intelligent analysis model for benign and malignant breast cancer can achieve the effect of intelligent analysis and classification of benign and malignant breast cancer based on the precision and recall rate of the AI-based intelligent analysis model for benign and malignant breast cancer; When the artificial intelligence-based intelligent analysis model for benign and malignant breast cancer cannot achieve the benign and malignant intelligent analysis classification effect of breast cancer, the parameters and structure of the artificial intelligence-based intelligent analysis model for benign and malignant breast cancer are continuously adjusted, the artificial intelligence-based intelligent analysis model for benign and malignant breast cancer is continuously iteratively optimized, and the performance of the artificial intelligence-based intelligent analysis model for benign and malignant breast cancer is verified based on the cross-validation method to achieve the best benign and malignant breast cancer classification effect and determine the optimal intelligent analysis model for benign and malignant breast cancer.

[0035] Among them, the intelligent analysis module is used to perform intelligent analysis on the benign and malignant condition of the patient's breast cancer to determine the intelligent analysis results of the benign and malignant condition of the patient's breast cancer.

[0036] In this embodiment, as a preferred technical solution of the present invention, the intelligent analysis module includes: A model deployment unit, used to deploy the optimal breast cancer benign and malignant intelligent analysis model in an actual breast cancer benign and malignant intelligent analysis environment; Intelligent analysis unit, used to intelligently analyze the benign and malignant status of the patient's breast cancer; The patient's real-time breast cancer data is input into the optimal intelligent analysis model for benign and malignant breast cancer. Based on the optimal intelligent analysis model for benign and malignant breast cancer, the patient's real-time breast cancer data is intelligently analyzed, and the benign and malignant conditions of the patient's breast cancer are classified to determine the intelligent analysis result of the patient's benign and malignant breast cancer, wherein the intelligent analysis result of the patient's benign and malignant breast cancer is that the patient's breast cancer is benign or the patient's breast cancer is malignant.

[0037] Among them, the intelligent analysis results of the patient's breast cancer are output through the output management module, and the intelligent analysis report of the patient's breast cancer is displayed in a visual form, and intelligent treatment management of the patient's breast cancer is carried out.

[0038] In this embodiment, as a preferred technical solution of the present invention, the output management module includes: The output display unit is used to output the intelligent analysis results of the benign and malignant nature of the patient's breast cancer, analyze the patient's real-time breast cancer data and the intelligent analysis results of the patient's breast cancer, generate an intelligent analysis report of the patient's breast cancer, and display the intelligent analysis report of the patient's breast cancer in a visual form, so that doctors and patients can more intuitively understand the benign and malignant nature of the patient's breast cancer; The analysis and management unit is used to intelligently manage the benign and malignant conditions of the patient's breast cancer. According to the intelligent analysis results of the patient's breast cancer, a breast cancer treatment plan is formulated for the patient, and intelligent treatment management of the patient's breast cancer is performed based on the breast cancer treatment plan.

[0039] In one embodiment, personal medical history data, medical imaging data, pathological tissue data, and genomics data are preprocessed to obtain preprocessed data; Extracting tissue pathology feature vectors based on pre-processed medical imaging data and pathological tissue data, and extracting gene mutation molecular feature vectors based on pre-processed genomics data; The tissue pathology feature vector and the gene mutation molecular feature vector are fused to obtain the fused multimodal features of the patient; Determine the differential gene expression parameters of the patient based on the fusion multimodal features, and determine the cell transcription profile expression data based on the differential gene expression parameters; Determine the patient's candidate mutation genes based on cell transcription profile expression data, and determine the patient's cancer signature based on the candidate mutation genes; Determine the mRNA data and miRNA data of the patient under cancerous lesions according to the cancer label, and obtain the patient's pathological image features based on the pre-processed medical imaging data; Reconstruct the pathological image features with the mRNA data and miRNA data to obtain the pathological image feature matrix, mRNA expression matrix and miRNA expression matrix; Construct a correlation structure diagram based on the pathological image feature matrix, mRNA expression matrix, and miRNA expression matrix; According to the association structure diagram, the pre-processed personal medical history data, medical imaging data, pathological tissue data and genomics data are labeled and packaged to generate an integrated file; Generate AI-based historical breast cancer data from integrated files.

[0040] The beneficial effects of the above technical solution are: by constructing an association structure diagram of the patient's cancer data and then annotating and packaging the personal medical history data, medical imaging data, pathological tissue data and genomics data, medical personnel can quickly characterize the patient's cancerous lesions based on the annotation results, thereby improving the work efficiency and experience of medical personnel. At the same time, it also effectively interprets the patient's in-depth symptoms, provides effective reference samples for medical personnel, and improves practicality.

[0041] In one embodiment, after acquiring the medical image data, the method further includes: Get the pixel gray value of each pixel in the medical image data, and determine the image chaos of the medical image according to the pixel gray value:

[0042]

[0043] Where Q represents the image chaos of the medical image, M represents the number of regions into which the medical image is divided into equal-area regions, and i represents the i-th divided region. It is represented as the average pixel gray value of the i-th divided area, and ln is represented as the natural logarithm;

[0044] Determine the uniformity of pixel grayscale distribution of the medical image according to the image chaos of the medical image, and confirm whether the medical image needs to be contrast enhanced according to the uniformity of pixel grayscale distribution;

[0045] If so, the contrast enhancement of medical images is achieved through the following formula:

[0046]

[0047] in, Represented as the grayscale value of the output medical image, Expressed as a dynamic coefficient, Represented as the grayscale value of the input medical image, It is expressed as the average value of grayscale in the neighborhood of point (i, j);

[0048] The enhanced medical images are used as medical imaging reference data.

[0049] The beneficial effect of the above technical solution is that the image quality and image display effect can be guaranteed by contrast enhancement of medical images, which lays a foundation for medical personnel to review and refer to, and further improves the practicality.

[0050] In summary, by collecting the patient's personal medical history data, medical imaging data, pathological tissue data and genomic data, the breast cancer historical data is determined, and by processing the breast cancer historical data, the breast cancer characteristic data is determined. Based on the optimal breast cancer benign and malignant intelligent analysis model, the patient's breast cancer real-time data is intelligently analyzed, and the patient's breast cancer benign and malignant conditions are classified to determine the patient's breast cancer benign and malignant intelligent analysis results, and the patient's breast cancer benign and malignant intelligent analysis results are output, and the patient's breast cancer benign and malignant intelligent analysis report is displayed in a visual form, so that doctors and patients can more intuitively understand the benign and malignant nature of the patient's breast cancer, can perform intelligent analysis and effectively identify and classify the benign and malignant nature of breast cancer, and can improve the effect of intelligent analysis of breast cancer benign and malignant.

[0051] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0052] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent analysis system for benign and malignant breast cancer based on artificial intelligence, characterized by: include: The data collection module is used to collect the patient's personal medical history data, medical imaging data, pathological tissue data and genomics data to determine the AI-based breast cancer historical data; A data processing module, used to process artificial intelligence-based breast cancer historical data and determine artificial intelligence-based breast cancer characteristic data; The model training module is used to construct an AI-based intelligent analysis model for benign and malignant breast cancer, and to test and optimize the AI-based intelligent analysis model for benign and malignant breast cancer to determine the optimal intelligent analysis model for benign and malignant breast cancer; Intelligent analysis module, used to intelligently analyze the benign and malignant conditions of the patient's breast cancer and determine the intelligent analysis results of the patient's benign and malignant breast cancer; The output management module is used to output the intelligent analysis results of the benign and malignant status of the patient's breast cancer, and to display the intelligent analysis report of the patient's breast cancer in a visual form, and to perform intelligent treatment management on the patient's breast cancer.

2. The artificial intelligence-based intelligent analysis system for benign and malignant breast cancer according to claim 1, characterized in that: After collecting the patient's personal medical history data, medical imaging data, pathological tissue data and genomic data, and determining the AI-based breast cancer history data, it also includes: Preprocess personal medical history data, medical imaging data, pathological tissue data and genomics data to obtain preprocessed data; Extracting tissue pathology feature vectors based on pre-processed medical imaging data and pathological tissue data, and extracting gene mutation molecular feature vectors based on pre-processed genomics data; The tissue pathology feature vector and the gene mutation molecular feature vector are fused to obtain the fused multimodal features of the patient; Determine the differential gene expression parameters of the patient based on the fusion multimodal features, and determine the cell transcription profile expression data based on the differential gene expression parameters; Determine the patient's candidate mutation genes based on cell transcription profile expression data, and determine the patient's cancer signature based on the candidate mutation genes; Determine the mRNA data and miRNA data of the patient under cancerous lesions according to the cancer label, and obtain the patient's pathological image features based on the pre-processed medical imaging data; Reconstruct the pathological image features with the mRNA data and miRNA data to obtain the pathological image feature matrix, mRNA expression matrix and miRNA expression matrix; Construct a correlation structure diagram based on the pathological image feature matrix, mRNA expression matrix, and miRNA expression matrix; According to the association structure diagram, the pre-processed personal medical history data, medical imaging data, pathological tissue data and genomics data are labeled and packaged to generate an integrated file; Generate AI-based historical breast cancer data from integrated files.

3. The artificial intelligence-based intelligent analysis system for benign and malignant breast cancer according to claim 1, characterized in that: The data collection module comprises: The personal medical history collection unit is used to collect the patient's age, gender, family medical history, past medical history, menstrual status and reproductive history to obtain personal medical history data; A medical image collection unit, used to collect breast X-rays, ultrasound images and magnetic resonance imaging of patients to obtain medical image data; Pathological tissue collection unit, used to collect the patient's pathological sections and immunohistochemistry to obtain pathological tissue data; A genomics collection unit is used to collect the patient's gene expression profile, gene mutation and amplified genetic information to obtain genomics data; Among them, artificial intelligence-based breast cancer historical data is determined based on personal medical history data, medical imaging data, pathological tissue data and genomics data.

4. The artificial intelligence-based intelligent analysis system for benign and malignant breast cancer according to claim 3, characterized in that: After obtaining medical imaging data, it also includes: Get the pixel gray value of each pixel in the medical image data, and determine the image chaos of the medical image according to the pixel gray value: Where Q represents the image chaos of the medical image, M represents the number of regions into which the medical image is divided into equal-area regions, and i represents the i-th divided region. It is represented as the average pixel gray value of the i-th divided area, and ln is represented as the natural logarithm; Determine the uniformity of pixel grayscale distribution of the medical image according to the image chaos of the medical image, and confirm whether the medical image needs to be contrast enhanced according to the uniformity of pixel grayscale distribution; If so, the contrast enhancement of medical images is achieved through the following formula: in, Represented as the grayscale value of the output medical image, Expressed as a dynamic coefficient, Represented as the grayscale value of the input medical image, It is expressed as the average value of grayscale in the neighborhood of point (i, j); The enhanced medical images are used as medical imaging reference data.

5. The artificial intelligence-based intelligent analysis system for benign and malignant breast cancer according to claim 3, characterized in that: The data processing module comprises: A data cleaning unit, used to clean historical breast cancer data based on artificial intelligence; Based on the data cleaning tool, duplicate values, missing values ​​and outliers in the AI-based breast cancer historical data are identified and processed; When duplicate values ​​exist in AI-based breast cancer historical data, the duplicate values ​​are removed; When missing values ​​exist in AI-based breast cancer historical data, samples containing missing values ​​are deleted or the median is used to fill the missing values; When outliers exist in AI-based breast cancer historical data, samples containing outliers are deleted or the average value is used to replace the outliers.

6. The artificial intelligence-based intelligent analysis system for benign and malignant breast cancer according to claim 5, characterized in that: The data processing module further includes: A data conversion unit for converting historical breast cancer data based on artificial intelligence; Perform format and type conversion on AI-based breast cancer historical data, remove dimensional differences between AI-based breast cancer historical data, and determine standardized breast cancer historical data; A feature extraction unit, used for extracting features from standardized breast cancer historical data; The random forest algorithm based on feature importance evaluation is used to evaluate the importance of the extracted features, evaluate the influence of the extracted features on the classification of benign and malignant breast cancer, select the features that can best distinguish benign and malignant breast cancer lesions, and determine the breast cancer feature data based on artificial intelligence.

7. The artificial intelligence-based intelligent analysis system for benign and malignant breast cancer according to claim 5, characterized in that: The model training module includes: A data division unit, used for dividing the artificial intelligence-based breast cancer feature data into a training set and a test set; A model building unit, used to build an AI-based intelligent analysis model for benign and malignant breast cancer; Based on deep learning technology, the training set and the benign and malignant breast cancer labels are input into the deep learning model. Through continuous training iterations, the deep learning model can autonomously learn the benign and malignant classification of breast cancer and determine the benign and malignant breast cancer intelligent analysis model based on artificial intelligence. The test set is input into the artificial intelligence-based intelligent analysis model of benign and malignant breast cancer, and the performance of the artificial intelligence-based intelligent analysis model of benign and malignant breast cancer is tested based on the test set to determine whether the artificial intelligence-based intelligent analysis model of benign and malignant breast cancer can achieve the benign and malignant breast cancer intelligent analysis classification effect.

8. The artificial intelligence-based intelligent analysis system for benign and malignant breast cancer according to claim 7, characterized in that: Determine whether the AI-based intelligent analysis model for breast cancer benign and malignant can achieve the effect of intelligent analysis and classification of breast cancer benign and malignant, including: Obtain the results of the performance test of the AI-based intelligent analysis model for benign and malignant breast cancer using the test set, and determine whether the AI-based intelligent analysis model for benign and malignant breast cancer can achieve the effect of intelligent analysis and classification of benign and malignant breast cancer based on the precision and recall rate of the AI-based intelligent analysis model for benign and malignant breast cancer; When the artificial intelligence-based intelligent analysis model for benign and malignant breast cancer cannot achieve the benign and malignant intelligent analysis classification effect of breast cancer, the parameters and structure of the artificial intelligence-based intelligent analysis model for benign and malignant breast cancer are continuously adjusted, the artificial intelligence-based intelligent analysis model for benign and malignant breast cancer is continuously iteratively optimized, and the performance of the artificial intelligence-based intelligent analysis model for benign and malignant breast cancer is verified based on the cross-validation method to achieve the best benign and malignant breast cancer classification effect and determine the optimal intelligent analysis model for benign and malignant breast cancer.

9. The artificial intelligence-based intelligent analysis system for benign and malignant breast cancer according to claim 8, characterized in that: The intelligent analysis module comprises: A model deployment unit, used to deploy the optimal breast cancer benign and malignant intelligent analysis model in an actual breast cancer benign and malignant intelligent analysis environment; Intelligent analysis unit, used to intelligently analyze the benign and malignant status of the patient's breast cancer; The patient's real-time breast cancer data is input into the optimal intelligent analysis model for benign and malignant breast cancer. Based on the optimal intelligent analysis model for benign and malignant breast cancer, the patient's real-time breast cancer data is intelligently analyzed, and the benign and malignant conditions of the patient's breast cancer are classified to determine the intelligent analysis result of the patient's benign and malignant breast cancer, wherein the intelligent analysis result of the patient's benign and malignant breast cancer is that the patient's breast cancer is benign or the patient's breast cancer is malignant.

10. The artificial intelligence-based intelligent analysis system for benign and malignant breast cancer according to claim 9, characterized in that: The output management module comprises: The output display unit is used to output the intelligent analysis results of the benign and malignant nature of the patient's breast cancer, analyze the patient's real-time breast cancer data and the intelligent analysis results of the patient's breast cancer, generate an intelligent analysis report of the patient's breast cancer, and display the intelligent analysis report of the patient's breast cancer in a visual form, so that doctors and patients can more intuitively understand the benign and malignant nature of the patient's breast cancer; The analysis and management unit is used to intelligently manage the benign and malignant conditions of the patient's breast cancer. According to the intelligent analysis results of the patient's breast cancer, a breast cancer treatment plan is formulated for the patient, and intelligent treatment management of the patient's breast cancer is performed based on the breast cancer treatment plan.

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