Multi-immunohistochemical staining and field E staining combined comprehensive identification method

Abstract

The invention relates to the technical field of biological medicine, and discloses a multi-immunohistochemical staining and field E staining combined comprehensive identification method, which comprises an image data acquisition module, a data analysis module, a data identification module and a data management module, the data analysis module calculates a data speed increase index Siy, a multi-immunohistochemical staining combined field E staining recognition accuracy index Ziy and a comprehensive efficiency increase index Wiy, the data recognition module judges the data transmission speed of the system according to the speed increase index Siy, and the data transmission speed of the system is judged according to the multi-immunohistochemical staining combined field E staining recognition accuracy index Ziy. The data accuracy after the multi-immunohistochemical staining unit and the field E staining unit are combined to work is judged, the comprehensive efficiency of the system is evaluated according to a comprehensive efficiency improvement index Wiy, and the data management module automatically issues a repair instruction to a unit area needing to be repaired according to the judgment data of the data identification module.

Description

Technical Field

[0001] The invention relates to the technical field of biomedicine, and in particular to a comprehensive identification method combining multiple immunohistochemical staining with Field's E staining. Background Art

[0002] Multiplex immunohistochemical staining is a technique that simultaneously or sequentially stains and images multiple antigens on a single tissue section. It is able to reveal the spatial relationship between proteins with cellular resolution and usually needs to be used in combination with specific chromogens such as AEC to enhance the staining effect and maintain tissue integrity. Field E staining is a special tissue staining method used to enhance the visualization of specific tissue structures or components. When this staining technique is combined with multiplex immunohistochemical staining, it can provide a more comprehensive tissue analysis and help understand complex biological systems such as cell-cell interactions in the tumor microenvironment. Although this technology provides support for the development of therapeutic strategies, the disadvantage of existing technologies is that the data processing speed and accuracy cannot be improved. Summary of the invention

[0003] 1. Technical issues to be resolved

[0004] In view of the shortcomings of the prior art, the present invention provides a comprehensive identification method of multiple immunohistochemical staining combined with Field's E staining, which has the advantages of improved data processing speed and accuracy, and solves the problem that the shortcomings of the prior art are that the data processing speed and accuracy cannot be improved.

[0005] (II) Technical solution

[0006] To achieve the above object, the present invention provides the following technical solutions: a comprehensive identification method of multiple immunohistochemical staining combined with Field's E staining, comprising an image data acquisition module, a data analysis module, a data identification module and a data management module;

[0007] The image data acquisition module includes a multiple immunohistochemical staining unit, a Tian E staining unit and an artificial intelligence auxiliary unit. The multiple immunohistochemical staining unit collects multiple immunohistochemical staining data through the Internet, microscopy and image acquisition equipment, and after data numbering, it is connected to the artificial intelligence auxiliary unit through the network. The Tian E staining unit collects Tian E staining data through the Internet, microscopy and image acquisition equipment, and after data numbering, it is connected to the artificial intelligence auxiliary unit through the network. The artificial intelligence auxiliary unit collects artificial intelligence auxiliary data through the Internet and a learning framework model, and after data numbering, it is connected to the data analysis module through the network. The image data acquisition module is connected to the data analysis module through the network;

[0008] The data analysis module includes a data speed improvement unit, an accuracy improvement unit and a comprehensive efficiency improvement unit. The multiple immunohistochemical staining data calculates a data speed improvement index Siy according to the multiple immunohistochemical staining data, Tian E staining data and artificial intelligence-assisted data. The accuracy improvement unit calculates a multiple immunohistochemical staining combined with Tian E staining recognition accuracy index Ziy according to the multiple immunohistochemical staining data, Tian E staining data and artificial intelligence-assisted data. The comprehensive efficiency improvement unit calculates a comprehensive efficiency improvement index Wiy according to the data speed improvement index Siy and the multiple immunohistochemical staining combined with Tian E staining recognition accuracy index Ziy. The data speed improvement unit, the accuracy improvement unit and the comprehensive efficiency improvement unit are connected to the data recognition module through a network;

[0009] The data recognition module determines the speed of system data transmission according to the speed improvement index Siy, the data recognition module determines the accuracy of the data after the multiple immunohistochemical staining unit and the Tian E staining unit work according to the multiple immunohistochemical staining combined with Tian E staining recognition accuracy index Ziy, the data recognition module evaluates the comprehensive efficiency of the system according to the comprehensive efficiency improvement index Wiy, and the data recognition module and the data management module are connected through a network;

[0010] The data management module automatically issues repair instructions to the unit area that needs to be repaired according to the determination data of the data identification module.

[0011] Preferably, the multiple immunohistochemical staining unit numbers the multiple immunohistochemical staining data and the multiple immunohistochemical staining data processing time according to the multiple immunohistochemical staining data characteristics, and the multiple immunohistochemical staining data and the multiple immunohistochemical staining data processing time are numbered Q x , T x .

[0012] Preferably, the field E dyeing unit numbers the field E dyeing data and the field E dyeing data processing time according to the field E dyeing data characteristics, and the field E dyeing data and the field E dyeing data processing time are numbered Q y , T y .

[0013] Preferably, the artificial intelligence assistance unit performs data numbering on the multiple immunohistochemical staining data, field E staining data, multiple immunohistochemical staining data processing time and field E staining data processing time after artificial intelligence assistance according to the artificial intelligence assistance data characteristics, and the multiple immunohistochemical staining data, field E staining data, multiple immunohistochemical staining data processing time and field E staining data processing time after artificial intelligence assistance are numbered Qx1 , Q y1 、T x1 、T y1 .

[0014] Preferably, the multiple immunohistochemical staining data calculates the data speed improvement index Siy based on the multiple immunohistochemical staining data, the field E staining data and the artificial intelligence assisted data, and the calculation formula is:

[0015]

[0016] In the formula, Siy represents the data speed improvement index, T x represents the processing time of multiple immunohistochemical staining data, T y represents the field E staining data processing time, T x1 、T y1 They represent the processing time of multiple immunohistochemical staining data after artificial intelligence assistance and the processing time of field E staining data, respectively. x1 、a y1 It indicates the weight of the multiple immunohistochemistry staining data processing time and the Field E staining data processing time in the system data speed improvement index.

[0017] Preferably, the accuracy improvement unit calculates the multiple immunohistochemical staining combined with Field E staining recognition accuracy index Ziy according to the multiple immunohistochemical staining data, Field E staining data and artificial intelligence-assisted data, and the calculation formula is:

[0018]

[0019] In the formula, Ziy represents the recognition accuracy index of multiple immunohistochemical staining combined with Field E staining, Q x Represents multiple immunohistochemical staining data, O y represents the field E staining data, Q x1 , O y1 Respectively represent the multiple immunohistochemical staining data and Tian E staining data after artificial intelligence assistance, b x1 、b y1 It indicates the weight of multiple immunohistochemistry staining data and Field E staining data in the system data speed improvement index.

[0020] Preferably, the comprehensive efficiency improvement unit calculates the comprehensive efficiency improvement index Wiy according to the data speed improvement index Siy and the multiple immunohistochemical staining combined with field E staining recognition accuracy index Ziy, and the calculation formula is:

[0021]

[0022] In the formula, Wiy represents the comprehensive efficiency improvement index, Siy represents the comprehensive efficiency improvement unit according to the data speed improvement index, Ziy represents the multiple immunohistochemical staining combined with Field E staining recognition accuracy index, θ, They represent the proportion of the multiple immunohistochemical staining combined with Field E staining recognition accuracy index Ziy and data speed improvement index Siy in the comprehensive efficiency improvement index,

[0023] Preferably, the data identification module determines the speed of system data transmission according to the speed improvement index Siy. When the data speed improvement index Siy<1, the data management module increases the value of the data speed improvement index Siy by changing the microscope model and increasing the number of image acquisition devices.

[0024] Preferably, the data recognition module determines the data accuracy after the combination of the multiple immunohistochemical staining unit and the Field E staining unit based on the multiple immunohistochemical staining combined with Field E staining recognition accuracy index Ziy. When the data recognition module recognizes that the multiple immunohistochemical staining combined with Field E staining recognition accuracy index Ziy<1, the data management module will automatically adjust the image contrast, brightness and edge numerical range.

[0025] Preferably, the data identification module evaluates the comprehensive efficiency of the system according to the comprehensive efficiency improvement index Wiy. When the comprehensive efficiency improvement index Wiy=1, it can be determined that the system is in the optimal state. On this basis, the number of layers of the neural network and the number of neurons in each layer are adjusted or an appropriate activation function is selected.

[0026] Compared with the prior art, the present invention provides a comprehensive identification method combining multiple immunohistochemical staining with Field's E staining, which has the following beneficial effects:

[0027] 1. The present invention calculates the data speed improvement index Siy, which represents the speed improvement ability of the data identified by multiple immunohistochemical staining combined with field E staining after being combined with artificial intelligence assistance. When the data speed improvement index Siy is transmitted to the data recognition module through the network, the data recognition module can monitor the data speed improvement index Siy in real time. When the data speed improvement index Siy<1, it indicates that the data processing time after artificial intelligence assistance is reduced compared with the original processing time, that is, the processing speed is improved. The data recognition module can automatically identify this change according to a preset intelligent threshold, and then transmit it to the data management module through the network. The data management module then increases the value of the data speed improvement index Siy by replacing the microscope and the image acquisition device.

[0028] 2. The present invention calculates the multiple immunohistochemical staining combined with Tian E staining recognition accuracy index Ziy according to the multiple immunohistochemical staining data, Tian E staining data and artificial intelligence-assisted data through the accuracy improvement unit. The index helps to evaluate the accuracy improvement after the combination of artificial intelligence-assisted multiple immunohistochemical staining and Tian E staining technology, and at the same time evaluates the specific impact of artificial intelligence assistance on data accuracy, so that the effect of artificial intelligence improvement can be specifically measured and compared. When the data recognition module recognizes that the multiple immunohistochemical staining combined with Tian E staining recognition accuracy index Ziy<1, and transmits the information to the data management module, the data management module will automatically adjust the improvement measures after receiving the information, adjust the contrast of the image to make the details clearer, adjust the brightness of the image to improve the visual effect of the image, use filters to enhance the edges and details of the image to make the image clearer, and use filters or deep learning methods to remove noise in the image to improve image quality. According to the above improvement measures, the overall recognition accuracy of the system is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of the structure of the present invention; DETAILED DESCRIPTION

[0030] 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.

[0031] See also Figure 1 , a comprehensive identification method of multiple immunohistochemical staining combined with Field's E staining, including an image data acquisition module, a data analysis module, a data identification module and a data management module;

[0032] The image data acquisition module includes a multiple immunohistochemical staining unit, a Tian E staining unit and an artificial intelligence auxiliary unit. The multiple immunohistochemical staining unit collects multiple immunohistochemical staining data through the Internet, microscopy and image acquisition equipment, and after data numbering, it is connected to the artificial intelligence auxiliary unit through the network. The Tian E staining unit collects Tian E staining data through the Internet, microscopy and image acquisition equipment, and after data numbering, it is connected to the artificial intelligence auxiliary unit through the network. The artificial intelligence auxiliary unit collects artificial intelligence auxiliary data through the Internet and a learning framework model, and after data numbering, it is connected to the data analysis module through the network. The image data acquisition module is connected to the data analysis module through the network;

[0033] The data analysis module includes a data speed improvement unit, an accuracy improvement unit and a comprehensive efficiency improvement unit. The multiple immunohistochemical staining data calculates a data speed improvement index Siy based on the multiple immunohistochemical staining data, Tian E staining data and artificial intelligence-assisted data. The accuracy improvement unit calculates a multiple immunohistochemical staining combined with Tian E staining recognition accuracy index Ziy based on the multiple immunohistochemical staining data, Tian E staining data and artificial intelligence-assisted data. The comprehensive efficiency improvement unit calculates a comprehensive efficiency improvement index Wiy based on the data speed improvement index Siy and the multiple immunohistochemical staining combined with Tian E staining recognition accuracy index Ziy. The data speed improvement unit, the accuracy improvement unit and the comprehensive efficiency improvement unit are connected to the data recognition module through a network.

[0034] The data recognition module determines the speed of system data transmission according to the speed improvement index Siy. The data recognition module determines the accuracy of the data after the multiple immunohistochemical staining unit and the Tian E staining unit work according to the multiple immunohistochemical staining combined with Tian E staining recognition accuracy index Ziy. The data recognition module evaluates the comprehensive efficiency of the system according to the comprehensive efficiency improvement index Wiy. The data recognition module and the data management module are connected through the network.

[0035] The data management module automatically issues repair instructions to the unit area that needs to be repaired based on the judgment data of the data identification module.

[0036] The multiple immunohistochemical staining unit numbers the multiple immunohistochemical staining data and the multiple immunohistochemical staining data processing time according to the multiple immunohistochemical staining data characteristics. The multiple immunohistochemical staining data and the multiple immunohistochemical staining data processing time are numbered Q x , T x .

[0037] The field E staining unit numbers the field E staining data and the field E staining data processing time according to the field E staining data characteristics. The field E staining data and the field E staining data processing time are numbered Q y , T y .

[0038] The artificial intelligence assistance unit numbers the multiple immunohistochemical staining data, Tian E staining data, multiple immunohistochemical staining data processing time and Tian E staining data processing time after artificial intelligence assistance according to the artificial intelligence assistance data characteristics. The multiple immunohistochemical staining data, Tian E staining data, multiple immunohistochemical staining data processing time and Tian E staining data processing time after artificial intelligence assistance are numbered as Q x1 , Q y1 , T x1 , Ty1 .

[0039] The data speed improvement index Siy is calculated based on the multiple immunohistochemical staining data, field E staining data and artificial intelligence assisted data. The calculation formula is:

[0040]

[0041] In the formula, Siy represents the data speed improvement index, T x represents the processing time of multiple immunohistochemical staining data, T y represents the field E staining data processing time, T x1 、T y1 They represent the processing time of multiple immunohistochemical staining data after artificial intelligence assistance and the processing time of field E staining data, respectively. x1 、a y1 It indicates the weight of the multiple immunohistochemical staining data processing time and the field E staining data processing time in the system data speed improvement index;

[0042] The advantages are: by calculating the data speed improvement index Siy, the index represents the speed improvement ability of the data identified by multiple immunohistochemical staining combined with field E staining after being combined with artificial intelligence assistance. When the data speed improvement index Siy is transmitted to the data recognition module through the network, the data recognition module can monitor the data speed improvement index Siy in real time. When the data speed improvement index Siy<1, it indicates that the data processing time after artificial intelligence assistance is reduced compared with the original processing time, that is, the processing speed is improved. The data recognition module can automatically identify this change according to the preset intelligent threshold, and then transmit it to the data management module through the network. The data management module then increases the value of the data speed improvement index Siy by changing the microscope model and increasing the number of image acquisition devices.

[0043] The accuracy improvement unit calculates the multiple immunohistochemical staining combined with Tian E staining recognition accuracy index Ziy based on the multiple immunohistochemical staining data, Tian E staining data and artificial intelligence assisted data. The calculation formula is:

[0044]

[0045] In the formula, Ziy represents the recognition accuracy index of multiple immunohistochemical staining combined with Field E staining, O x Represents multiple immunohistochemical staining data, O y represents the field E staining data, O x1 , O y1 Respectively represent the multiple immunohistochemical staining data and Tian E staining data after artificial intelligence assistance, bx1 、b y1 Indicates the weight of multiple immunohistochemical staining data and field E staining data in the system data speed improvement index;

[0046] The advantages are: the accuracy improvement unit calculates the multiple immunohistochemical staining combined with Tian E staining recognition accuracy index Ziy according to the multiple immunohistochemical staining data, Tian E staining data and artificial intelligence-assisted data. This index helps to evaluate the accuracy improvement after the combination of artificial intelligence-assisted multiple immunohistochemical staining and Tian E staining technology, and at the same time evaluates the specific impact of artificial intelligence assistance on data accuracy, so that the effect of artificial intelligence improvement can be specifically measured and compared. When the data recognition module recognizes that the multiple immunohistochemical staining combined with Tian E staining recognition accuracy index Ziy<1, and transmits the information to the data management module, the data management module will automatically adjust the improvement measures after receiving the information, adjust the contrast of the image to make the details clearer, adjust the brightness of the image to improve the visual effect of the image, use filters to enhance the edges and details of the image to make the image clearer, and use filters or deep learning methods to remove noise in the image to improve image quality. According to the above improvement measures, the overall recognition accuracy of the system is improved.

[0047] The comprehensive efficiency improvement unit calculates the comprehensive efficiency improvement index Wiy according to the data speed improvement index Siy and the multiple immunohistochemical staining combined with field E staining recognition accuracy index Ziy. The calculation formula is:

[0048]

[0049] In the formula, Wiy represents the comprehensive efficiency improvement index, Siy represents the comprehensive efficiency improvement unit according to the data speed improvement index, Ziy represents the multiple immunohistochemical staining combined with Field E staining recognition accuracy index, θ, They represent the proportion of the multiple immunohistochemical staining combined with Field E staining recognition accuracy index Ziy and data speed improvement index Siy in the comprehensive efficiency improvement index,

[0050] The advantages are: by calculating the comprehensive efficiency improvement index Wiy, it is helpful to establish a real-time monitoring and feedback mechanism, regularly evaluate the comprehensive efficiency improvement index, and then combine the data recognition module to monitor the data speed improvement index Siy and the multiple immunohistochemical staining combined with Tian E staining recognition accuracy index Ziy to feedback recognition information for comprehensive efficiency evaluation. When the comprehensive efficiency improvement index Wiy=1, it can be determined that the system is in the best state. On this basis, the number of neural network layers and the number of neurons in each layer are optimized to capture more complex features, or appropriate activation functions (such as ReLU, Sigmoid, Tanh) are selected to improve model performance parameters. The above measures can effectively improve the comprehensive efficiency of multiple immunohistochemical staining and Tian E staining technology combined with artificial intelligence assistance, thereby providing the system with a tool to integrate multiple performance indicators.

[0051] The data identification module determines the speed of system data transmission according to the speed improvement index Siy. When the data speed improvement index Siy<1, the data management module increases the value of the data speed improvement index Siy by changing the microscope model and increasing the number of image acquisition devices.

[0052] The data recognition module determines the data accuracy of the combination of multiple immunohistochemical staining units and Tian E staining units based on the multiple immunohistochemical staining combined with Tian E staining recognition accuracy index Ziy. When the data recognition module recognizes that the multiple immunohistochemical staining combined with Tian E staining recognition accuracy index Ziy is <1, the data management module will automatically adjust the improvement measures by adjusting the contrast of the image to make the details clearer, adjusting the brightness of the image to improve the visual effect of the image, using filters to enhance the edges and details of the image to make the image clearer, and using filters or deep learning methods to remove noise in the image to improve image quality.

[0053] The data recognition module evaluates the comprehensive efficiency of the system according to the comprehensive efficiency improvement index Wiy. When the comprehensive efficiency improvement index Wiy=1, it can be determined that the system is in the best state. On this basis, the number of neural network layers and the number of neurons in each layer are optimized to capture more complex features, or appropriate activation functions (such as ReLU, Sigmoid, Tanh) are selected to improve the model performance parameters.

[0054] 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. A comprehensive identification method combining multiple immunohistochemical staining and field E staining, characterized in that: It includes image data acquisition module, data analysis module, data recognition module and data management module; The image data acquisition module includes a multiple immunohistochemical staining unit, a Tian E staining unit and an artificial intelligence auxiliary unit. The multiple immunohistochemical staining unit collects multiple immunohistochemical staining data through the Internet, microscopy and image acquisition equipment, and after data numbering, it is connected to the artificial intelligence auxiliary unit through the network. The Tian E staining unit collects Tian E staining data through the Internet, microscopy and image acquisition equipment, and after data numbering, it is connected to the artificial intelligence auxiliary unit through the network. The artificial intelligence auxiliary unit collects artificial intelligence auxiliary data through the Internet and a learning framework model, and after data numbering, it is connected to the data analysis module through the network. The image data acquisition module is connected to the data analysis module through the network; The data analysis module includes a data speed improvement unit, an accuracy improvement unit and a comprehensive efficiency improvement unit. The multiple immunohistochemical staining data calculates a data speed improvement index Siy according to the multiple immunohistochemical staining data, Tian E staining data and artificial intelligence-assisted data. The accuracy improvement unit calculates a multiple immunohistochemical staining combined with Tian E staining recognition accuracy index Ziy according to the multiple immunohistochemical staining data, Tian E staining data and artificial intelligence-assisted data. The comprehensive efficiency improvement unit calculates a comprehensive efficiency improvement index Wiy according to the data speed improvement index Siy and the multiple immunohistochemical staining combined with Tian E staining recognition accuracy index Ziy. The data speed improvement unit, the accuracy improvement unit and the comprehensive efficiency improvement unit are connected to the data recognition module through a network; The data recognition module determines the speed of system data transmission according to the speed improvement index Siy, the data recognition module determines the accuracy of the data after the multiple immunohistochemical staining unit and the Tian E staining unit work according to the multiple immunohistochemical staining combined with Tian E staining recognition accuracy index Ziy, the data recognition module evaluates the comprehensive efficiency of the system according to the comprehensive efficiency improvement index Wiy, and the data recognition module and the data management module are connected through a network; The data management module automatically issues repair instructions to the unit area that needs to be repaired according to the determination data of the data identification module.

2. The method of multiple immunohistochemical staining combined with field E staining for comprehensive identification according to claim 1, characterized in that: The multiple immunohistochemical staining unit numbers the multiple immunohistochemical staining data and the multiple immunohistochemical staining data processing time according to the multiple immunohistochemical staining data characteristics, and the multiple immunohistochemical staining data and the multiple immunohistochemical staining data processing time are numbered Q x , T x .

3. The comprehensive identification method of multiple immunohistochemical staining combined with Field's E staining according to claim 1, characterized in that: The field E dyeing unit numbers the field E dyeing data and the field E dyeing data processing time according to the field E dyeing data characteristics, and the field E dyeing data and the field E dyeing data processing time are numbered Q y , T y .

4. The comprehensive identification method of multiple immunohistochemical staining combined with Field's E staining according to claim 1, characterized in that: The artificial intelligence assistance unit performs data numbering on the multiple immunohistochemical staining data, field E staining data, multiple immunohistochemical staining data processing time and field E staining data processing time after artificial intelligence assistance according to the artificial intelligence assistance data characteristics, and the multiple immunohistochemical staining data, field E staining data, multiple immunohistochemical staining data processing time and field E staining data processing time after artificial intelligence assistance are respectively numbered as Q x1 , Q y1 , T x1 , T y1 .

5. The comprehensive identification method of multiple immunohistochemical staining combined with Field's E staining according to claim 3, characterized in that: The multiple immunohistochemical staining data calculates the data speed improvement index Siy based on the multiple immunohistochemical staining data, the field E staining data and the artificial intelligence assisted data, and the calculation formula is: In the formula, Siy represents the data speed improvement index, T x represents the processing time of multiple immunohistochemical staining data, T y represents the field E staining data processing time, T x1 , T y1 They represent the processing time of multiple immunohistochemical staining data after artificial intelligence assistance and the processing time of field E staining data, respectively. x1 、a y1 It indicates the weight of the multiple immunohistochemistry staining data processing time and the Field E staining data processing time in the system data speed improvement index.

6. The method of multiple immunohistochemical staining combined with field E staining comprehensive identification according to claim 3, characterized in that: The accuracy improvement unit calculates the multiple immunohistochemical staining combined with Field E staining recognition accuracy index Ziy according to the multiple immunohistochemical staining data, Field E staining data and artificial intelligence auxiliary data, and the calculation formula is: In the formula, Ziy represents the recognition accuracy index of multiple immunohistochemical staining combined with Field E staining, O x Represents multiple immunohistochemical staining data, O y represents the field E staining data, O x1 , O y1 Respectively represent the multiple immunohistochemical staining data and Tian E staining data after artificial intelligence assistance, b x1 、b y1 It indicates the weight of multiple immunohistochemistry staining data and Field E staining data in the system data speed improvement index.

7. The comprehensive identification method of multiple immunohistochemical staining combined with Field's E staining according to claim 6, characterized in that: The comprehensive efficiency improvement unit calculates the comprehensive efficiency improvement index Wiy according to the data speed improvement index Siy and the multiple immunohistochemical staining combined with field E staining recognition accuracy index Ziy, and the calculation formula is: In the formula, Wiy represents the comprehensive efficiency improvement index, Siy represents the comprehensive efficiency improvement unit according to the data speed improvement index, Ziy represents the multiple immunohistochemical staining combined with Field E staining recognition accuracy index, θ, They respectively represent the proportion of the multiple immunohistochemical staining combined with Field E staining recognition accuracy index Ziy and data speed improvement index Siy in the comprehensive efficiency improvement index, 8. The comprehensive identification method of multiple immunohistochemical staining combined with Field's E staining according to claim 5, characterized in that: The data identification module determines the speed of system data transmission according to the speed improvement index Siy. When the data speed improvement index Siy<1, the data management module increases the value of the data speed improvement index Siy by changing the microscope model and increasing the number of image acquisition devices.

9. The method of multiple immunohistochemical staining combined with field E staining comprehensive identification according to claim 6, characterized in that: The data recognition module determines the data accuracy after the multiple immunohistochemical staining unit and the Field E staining unit work together according to the multiple immunohistochemical staining combined with Field E staining recognition accuracy index Ziy. When the data recognition module recognizes that the multiple immunohistochemical staining combined with Field E staining recognition accuracy index Ziy is less than 1, the data management module will automatically adjust the image contrast, brightness and edge value range.

10. The comprehensive identification method of multiple immunohistochemical staining combined with Field's E staining according to claim 7, characterized in that: The data recognition module evaluates the comprehensive efficiency of the system according to the comprehensive efficiency improvement index Wiy. When the comprehensive efficiency improvement index Wiy=1, it can be determined that the system is in the best state. On this basis, the number of layers of the neural network and the number of neurons in each layer are adjusted or an appropriate activation function is selected.

Images