1. An artificial intelligence aided diagnosis system for blood diseases based on bone marrow cell morphology, which is characterized by comprising: the image acquisition module is used for acquiring a Swiss staining bone marrow image, a cytochemical staining bone marrow image and a peripheral blood image; the image analysis module is used for inputting the Reye stained bone marrow image, the cytochemical stained bone marrow image and the peripheral blood image into a trained deep network model, and learning the effective characteristics of bone marrow morphology by fusing medical diagnosis professional knowledge to obtain relevant parameters of the deep learning-based case bone marrow morphological effective characteristics; and the disease type judging module is used for analyzing the bone marrow morphological parameters of the case and identifying the disease type of the case to be diagnosed by adopting a machine learning method based on knowledge reasoning and clinical experience fusion.

2. The artificial intelligent assistant diagnosis system for blood diseases based on bone marrow cell morphology, which is characterized in that the Reynolds-stained bone marrow image is a microscopic image acquired by a high power microscope and an oil microscope of the Reynolds-stained bone marrow smear for a case to be diagnosed; the cytochemical staining bone marrow and peripheral blood images are microscopic images which are acquired by adopting an oil microscope aiming at the cytochemical staining bone marrow and the peripheral blood smear of a case to be diagnosed.

3. The artificial intelligence aided diagnosis system for blood diseases based on bone marrow cell morphology as claimed in claim 1, wherein the image analysis module is used for inputting the Reye stained bone marrow image and the cytochemical stained bone marrow and peripheral blood image into a trained deep network model to identify each type of bone marrow cells, and fusing medical diagnosis professional knowledge to learn three effective features of bone marrow morphology: bone marrow hyperplasia degree, bone marrow nucleated cell proportion and cytochemical staining degree, and calculating relevant parameters of effective characteristics of bone marrow morphology of cases based on deep learning.

4. The artificial intelligence aided diagnosis system for blood diseases based on bone marrow cell morphology as claimed in claim 3, wherein the image analysis module comprises: the myeloproliferation degree evaluation module is used for analyzing the Reye stained bone marrow image under the high-power microscope through the trained depth network model and judging the myeloproliferation degree of a case according to the number of nucleated cells under the visual field of a single high-power microscope; the marrow nucleated cell classification module is used for analyzing the Swiss staining marrow image under the oil scope through the trained deep network model, and automatically classifying and counting the number of the marrow nucleated cells to obtain the proportion of various types of nucleated cells in the marrow; and the cytochemical staining evaluation module is used for analyzing the cytochemical staining bone marrow and peripheral blood images through the trained deep network model and establishing a case cytochemical staining integral system according to the positive degree of the cytochemical staining.

5. The artificial intelligence aided diagnosis system for blood diseases based on bone marrow cell morphology as claimed in claim 4, wherein the myeloproliferation degree evaluation module obtains the visual field area of a single high power lens by merging K images of Reye's stained bone marrow under the high power lens,wherein S is₁Is the area of a single high power lens view, S₂For the actual area of the Reye stained bone marrow image under the single high power lens, FN is the number of ocular views, n is the magnification of the objective lens, and D is the camera chip scaleIdentifying nucleated cells of the image of the single high power lens visual field area through the trained deep network model to obtain the number of the nucleated cells under the single high power lens visual field, judging the myeloproliferation degree according to the number of the nucleated cells, wherein,n represents the number of nucleated cells under a single high power field.

6. The artificial intelligence aided diagnosis system for blood diseases based on bone marrow cell morphology as claimed in claim 4, wherein said bone marrow nucleated cell classification module adopts said trained deep network model to automatically classify the nucleated cells in said Thoresian-stained bone marrow image under the oil microscope, counts the continuous predetermined number of nucleated cells, and calculates the ratio of each type of nucleated cells.

7. The artificial intelligence aided diagnosis system for blood diseases based on bone marrow cell morphology as claimed in claim 4, wherein the cytochemical staining evaluation module employs the trained deep network model to automatically classify the target cells in the bone marrow and peripheral blood images with different types of cytochemical staining according to different staining degrees, and calculates the total integral of staining degrees of a predetermined number of target cells according to corresponding scoring rules to represent the overall reaction of the case to the cytochemical staining.

8. The artificial intelligence aided diagnosis system for blood diseases based on bone marrow cell morphology as claimed in claim 1, wherein the disease type determination module comprises: the characteristic extraction module is used for extracting case characteristics according to the morphological diagnosis characteristics of the disease marrow; and the case classification module is used for judging the disease type of the case by adopting a machine learning method based on knowledge reasoning and clinical experience fusion according to the case characteristics.

Background

The bone marrow morphological examination is the necessary basis for the diagnosis of the blood system diseases, and the diagnosis, the curative effect and the prognosis evaluation of the blood system diseases are determined by observing bone marrow cells through a microscope in clinical examination work and classifying, counting and morphologically observing various hematopoietic cells of normal or abnormal forms at different developmental stages. Heretofore, manual microscopic examination is still the main method for bone marrow morphological examination, and the examination process is time-consuming, labor-consuming and inefficient. In recent years, the artificial intelligence technology is rapidly developed in the medical field, the artificial intelligence technology is combined with bone marrow morphological diagnosis, beneficial results are expected to be obtained in the aspect of development of an AI (artificial intelligence) auxiliary diagnosis system for blood system diseases, the diagnosis accuracy and the work efficiency are improved, and the pressure of clinical examination work is reduced.

The artificial intelligence method applied to the morphological diagnosis of the bone marrow of the blood system diseases mostly focuses on the identification of the types of the bone marrow nucleated cells, and the diagnosis method is deficient in the aspects of medical scientificity and real case application. The patent with the application number of CN202010126812.6 discloses a marrow cell image artificial intelligence auxiliary grading diagnosis system and a method, which comprises a data acquisition unit, an automatic identification and labeling unit, a manual labeling unit, a cell data statistics unit, a data analysis grading unit, an image auxiliary grading diagnosis unit, a display, a storage module and a processor. The artificial intelligent auxiliary analysis and diagnosis system only counts the number of bone marrow nucleated cells in the data counting unit, and the data analysis and classification unit only takes the ratio of granulocytes to erythrocytes and the proportion of various cells in the bone marrow nucleated cells as analysis data, thereby neglecting the analysis of the myeloproliferation degree and the positive degree of various cell chemical staining.

Disclosure of Invention

The invention aims to provide an artificial intelligent auxiliary diagnosis system for blood diseases based on bone marrow cell morphology, and the artificial intelligent auxiliary diagnosis system can realize high-accuracy AI diagnosis of bone marrow morphology.

The technical scheme adopted by the invention for solving the technical problems is as follows: provided is a blood disease artificial intelligence auxiliary diagnosis system based on bone marrow cell morphology, comprising: the image acquisition module is used for acquiring a Swiss staining bone marrow image, a cytochemical staining bone marrow image and a peripheral blood image; the image analysis module is used for inputting the Reye stained bone marrow image, the cytochemical stained bone marrow image and the peripheral blood image into a trained deep network model, and learning the effective characteristics of bone marrow morphology by fusing medical diagnosis professional knowledge to obtain relevant parameters of the deep learning-based case bone marrow morphological effective characteristics; and the disease type judging module is used for analyzing the bone marrow morphological parameters of the case and identifying the disease type of the case to be diagnosed by adopting a machine learning method based on knowledge reasoning and clinical experience fusion.

The Swiss staining bone marrow image is a microscopic image acquired by a high power microscope and an oil microscope aiming at a Swiss staining bone marrow smear of a case to be diagnosed; the cytochemical staining bone marrow and peripheral blood images are microscopic images which are acquired by adopting an oil microscope aiming at the cytochemical staining bone marrow and the peripheral blood smear of a case to be diagnosed.

The image analysis module is used for inputting the Reye stained bone marrow image, the cytochemical stained bone marrow image and the peripheral blood image into a trained deep network model to identify various types of bone marrow cells, and combining medical diagnosis professional knowledge to learn three effective characteristics of bone marrow morphology: bone marrow hyperplasia degree, bone marrow nucleated cell proportion and cytochemical staining degree, and calculating relevant parameters of effective characteristics of bone marrow morphology of cases based on deep learning.

The image analysis module includes: the myeloproliferation degree evaluation module is used for analyzing the Reye stained bone marrow image under the high-power microscope through the trained depth network model and judging the myeloproliferation degree of a case according to the number of nucleated cells under the visual field of a single high-power microscope; the marrow nucleated cell classification module is used for analyzing the Swiss staining marrow image under the oil scope through the trained deep network model, and automatically classifying and counting the number of the marrow nucleated cells to obtain the proportion of various types of nucleated cells in the marrow; and the cytochemical staining evaluation module is used for analyzing the cytochemical staining bone marrow and peripheral blood images through the trained deep network model and establishing a case cytochemical integral system according to the positive degree of the cytochemical staining.

The myeloproliferation degree evaluation module obtains the visual field area of a single high-power microscope in a mode of combining K pieces of Thauer's stained bone marrow images under the high-power microscope, wherein,wherein S is₁Is the area of a single high power lens view, S₂Performing nucleated cell identification on the image of the single high power lens visual field area through the trained depth network model to obtain the number of nucleated cells under the single high power lens visual field, and judging the myeloproliferation degree according to the number of the nucleated cells, wherein the actual area of the Ruhrstan's stained bone marrow image under the single high power lens is FN (eyepiece field number), n is objective lens magnification, and D is the size of a camera chip,

n represents the number of nucleated cells under a single high power field.

The marrow nucleated cell classification module adopts the trained deep network model to automatically classify the nucleated cells in the Reye stained marrow image under the oil scope, counts a preset number of continuous nucleated cells, and calculates the proportion of various nucleated cells.

The cytochemical staining evaluation module adopts the trained deep network model to automatically classify target cells in different types of cytochemical staining bone marrow and peripheral blood images according to different staining degrees, and calculates the total integral of the staining degrees of a preset number of target cells according to corresponding scoring rules so as to represent the integral reaction of a case to the cytochemical staining.

The disease type judging module comprises: the characteristic extraction module is used for extracting case characteristics according to the morphological diagnosis characteristics of the disease marrow; and the case classification module is used for judging the disease type of the case by adopting a machine learning method based on knowledge reasoning and clinical experience fusion according to the case characteristics.

Advantageous effects

Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages and positive effects:

the invention applies the artificial intelligence technology to a whole set of flow of bone marrow morphological image recognition and disease analysis, automatically generates a diagnosis result for the disease of the blood system, can improve the working efficiency and reduce the pressure of clinical examination work.

The operation object of the image acquisition module covers smears with different dyeing types, the Reye dyeing bone marrow image is acquired, the cytochemical dyeing bone marrow and peripheral blood images are acquired, and all types of images are input into the image analysis module to improve the diagnosis accuracy rate of the blood diseases based on the bone marrow morphology. The image recognition method of the AI image analysis module of the system is a deep learning algorithm, is different from the prior art in that the system integrates medical diagnosis professional knowledge except for analyzing marrow nucleated cell classification and proportion, increases the evaluation on myeloproliferation degree and cytochemical staining positive degree, calculates relevant parameters of effective characteristics of bone marrow morphology of a case based on deep learning, obtains more comprehensive bone marrow morphological parameters aiming at a case to be diagnosed, and ensures that an AI auxiliary diagnosis result is more reliable.

The case classification module is based on a machine learning algorithm, the original data of extracted case characteristic parameters are used as classification characteristics, the morphological effective characteristics of case marrow obtained based on knowledge fusion are fully utilized in the case classification process, the morphological effective characteristics of the case marrow comprise myeloproliferation degree, disease characteristic nucleated cell proportion and disease characteristic cytochemical staining type positive degree, the professional standards of clinical diagnosis and clinical big data are fused, the association between the myelomorphological effective characteristics and blood disease types is learned by using a knowledge reasoning method, a blood disease type classifier model based on the obtained association is established, the myelomorphological characteristics of the case are input into a well-learned disease type classifier based on the machine learning algorithm, and the blood disease type of the case to be diagnosed is output. Compared with other bone marrow morphological AI auxiliary diagnosis systems, the system learns and adopts more and more comprehensive bone marrow morphological characteristic parameters based on medical diagnosis professional knowledge, the obtained bone marrow morphological AI auxiliary diagnosis result of the blood system diseases is more scientific and interpretable, and no precedent for comprehensively utilizing the parameters to carry out bone marrow morphological AI auxiliary diagnosis exists before.

Drawings

FIG. 1 is a schematic structural view of the present invention;

FIG. 2 is a schematic structural diagram of a deep network model in an embodiment of the present invention;

fig. 3 is a schematic diagram of a case classifier model in an embodiment of the present invention.

Detailed Description

The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.

An embodiment of the present invention relates to an artificial intelligence aided diagnosis system for blood diseases based on bone marrow cell morphology, as shown in fig. 1, comprising: the image acquisition module is used for acquiring a Swiss staining bone marrow image, a cytochemical staining bone marrow image and a peripheral blood image; the image analysis module is used for inputting the Reye stained bone marrow image, the cytochemical stained bone marrow image and the peripheral blood image into a trained deep network model, and learning the effective characteristics of bone marrow morphology by fusing medical diagnosis professional knowledge to obtain relevant parameters of the deep learning-based case bone marrow morphological effective characteristics; and the disease type judging module is used for analyzing the bone marrow morphological parameters of the case and identifying the disease type of the case to be diagnosed by adopting a machine learning method based on knowledge reasoning and clinical experience fusion.

An image acquisition module: the microscopic image acquisition system is used for acquiring microscopic images of a Reye staining bone marrow smear, a cytochemical staining bone marrow and a peripheral blood smear aiming at a case to be diagnosed according to different magnifications, and comprises a Reye staining bone marrow image under a high power microscope and an oil microscope and a cytochemical staining bone marrow and peripheral blood image under the oil microscope. The collection of the image simulates the mode of manual microscopic examination, a Swiss staining bone marrow image with a visual field area is collected under a high power microscope, a proper area at the body tail junction is selected in a Swiss staining bone marrow smear and a cytochemical staining smear under an oil microscope, the objective table is moved in an N-shaped mode, and meanwhile, the image is continuously collected. The acquired Reye staining bone marrow image under the high power microscope can reflect the distribution of nucleated cells under a high power microscope visual field; the acquired images of the Ruhrstan's stained bone marrow under the oil lens comprise at least 500 nucleated cells which are continuously distributed in a proper area; the collected cytochemical staining images under the oil lens contain at least 100 target cells distributed in a suitable area in series. It is worth mentioning that the high power mirror in this embodiment has a magnification of 40 times, and the oil mirror has a magnification of 100 times.

Among them, the cytochemical staining generally includes neutral granulocyte alkaline phosphatase (NAP/AKP) staining, Peroxidase (POX) staining, periodic acid-Schiff (PAS) staining, Naphthol AS-D naphthol acetate esterase (NAS-DCE) staining, NAS-D naphthol acetate esterase (NAS-DAE) staining, and Iron (Iron) staining.

Taking Chronic myelogenous leukemia (CML-CP) as an example, the AKP score of a peripheral blood smear is reduced to be one of the morphological characteristics of the marrow, and the AKP score is obtained by classifying, counting and assigning 100 continuous mature neutrophils in the peripheral blood smear stained by the AKP according to the staining degree. Therefore, when the system needs to judge whether the case is CML-CP, the cytochemical staining image collected in the previous stage should comprise AKP staining peripheral blood image.

An image analysis module: the module utilizes an artificial intelligence technology to carry out image recognition and data processing on the acquired microscopic images of different types, and recognizes target cells based on a deep learning algorithm to obtain related parameters of case bone marrow morphology, wherein the deep learning algorithm used in the embodiment is a fast-RCNN network. The Faster RCNN, which is representative of the two-stage target detection method, is composed of four modules as shown in fig. 2. The first module is a backbone network, which is a common deep network such as VGG or ResNet, and removes a full connection layer used for classification at the end for feature extraction. The second module is a candidate box generation network for generating target candidate boxes. The third module is ROI pooling, the first two modules are combined, the feature maps with the same size are extracted from target candidate frames with different sizes, and the feature maps are sent to the last module. The fourth module is a classification and regression network. And respectively adding the input features into a classification head and a regression head, wherein the classification head is used for predicting the category of the target, and the regression head is used for adjusting the position of the candidate frame. The network implementation is as follows: the image input size is 224 × 224 × 224, the number of candidate frames generated by each layer of RPN of the feature pyramid during training is 2000, the number of candidate frames during testing is 1000, the score threshold of the candidate frames during testing is 0.15, the non-maximum suppression NMS threshold is 0.7, and max _ iter is 40000. The network loss function is as follows:

wherein the first half of the formula is used for classification, so a cross-entropy loss function, p, is used_iIs the probability that the ith initial candidate box is predicted to be the target. p is a radical of_i ^*The value of (b) is different according to the situation of the real frame corresponding to the ith candidate frame. When the IOU of the ith candidate frame and the corresponding real frame is more than 0.7, p_i ^*A candidate box that meets such a condition is also referred to as a positive sample 1. Since the number of candidate frames generated in the RPN network is tens of thousands, only a part (for example, two thousand) of the candidate frames need to be taken as training positive samples, but since the number of candidate frames with an IOU greater than 0.7 is less than two thousand, after a part of the candidate frames with an IOU greater than 0.7 is taken as positive samples, the candidate frames with the largest IOU among the remaining candidate frames are sequentially taken as positive samples until the number of required two thousand positive samples is made up. After taking the positive samples, some candidate boxes with IOU less than 0.3 corresponding to the real box need to be found as negative samples, and p corresponding to the negative samples_i ^*Is equal to 0, namelyCandidate boxes that are neither positive nor negative samples are not involved in training.

The second half of the formula is the loss function in the bounding box regression. After the sum of the loss functions of the two parts is obtained, the sum can be used for parameter training.

And the myeloproliferation degree evaluation module analyzes the Reynold's stained bone marrow image (40x) under the high-power microscope through a trained depth network model, identifies nucleated cells under the visual field of a single high-power microscope, and judges the myeloproliferation degree of a case according to the number of the nucleated cells. During the conversion of the microscope field of view into a digitized image, the actual area represented by a single digitized microscope image is much smaller than the area of the field of view under the microscope. Eyepiece lens of a Zeiss Axio scope 1 microscope used in the present embodimentThe field of view number of (2) is 23, and the field of view area under the high power lens is 0.2595mm through calculation²In a calculation manner ofWherein S is₁The field area of a single high power lens, FN, the number of the ocular fields and n, the magnification of the objective lens. The AxioCam HRc camera used for collecting microscopic images has a chip size of 2/3 inches, a middle mirror magnification of 1.0x, and a real area represented by each collected image of 0.03412mm²In a calculation manner ofWherein S is₂The actual area of the single swiss stained bone marrow image (40x) is shown, n is the objective magnification, and D is the camera chip size. The actual area size represented by the 7 microscope images was scaled to approximately equal the area under a high power field of view. Therefore, for each case, 7 images are required to be continuously acquired in layout, which represents a high power lens field, and the total number of nucleated cells in the 7 images replaces the number of nucleated cells in the high power lens field to be used as the basis for judging the myeloproliferation degree.

When the judgment is carried out, a bone marrow nucleated cell AI identification model based on fast RCNN is constructed, nucleated cells in a high-power lens visual field area are identified and counted, and the myeloproliferation degree of a case is judged. Nucleated cells in 7 pictures can be detected through the fast RCNN network, and the myeloproliferation degree can be judged according to the number of the nucleated cells.

Wherein the content of the first and second substances,n represents the number of nucleated cells under a single high power field.

The marrow nucleated cell classification module adopts a trained deep network model to classify nucleated cells of different development stages of each cell line in an oil-lens Thauberg staining marrow image (100x) and counts the number of the nucleated cells to obtain the proportion of various nucleated cells in the marrow. When a Swiss staining marrow image (100x) is collected for a case to be detected, a proper area at the body tail junction is selected in a marrow smear in a mode of simulating artificial microscopy, an objective table is moved in an N-shaped mode, simultaneously, under-oil-scope images are continuously collected, a microscopic image at least containing 500 nucleated cells is collected for each case and is used as a basis for judging the proportions of various nucleated cells in the marrow of the case, the marrow nucleated cells in the marrow can be automatically classified based on the Faster-RCNN network, and therefore the proportions of various nucleated cells in the marrow are counted.

The cytochemical staining evaluation module adopts a trained deep network model to classify target cells with different staining degrees in different types of cytochemical staining bone marrow and peripheral blood images under an oil scope, and establishes a case cytochemical integral system according to the positive degree of cell staining. Taking AKP staining peripheral blood smear as an example, for the acquisition of the AKP staining peripheral blood smear microscopic image of the case to be detected, selecting a suitable area of a body-tail boundary, moving the objective table according to an N shape, continuously acquiring the microscopic image under an oil lens, acquiring the microscopic image at least containing 100 mature neutrophils for each case to be detected, and taking the group of images as the image basis for judging the AKP integral of the case to be detected.

During judgment, an AKP integral AI calculation model based on fast RCNN is constructed for automatically classifying mature neutrophils in the peripheral blood smear microscopic image stained by AKP according to different staining degrees, and the grading of the staining degrees of the cells and the corresponding scoring rules areThe AKP score refers to the total score of the AKP stained peripheral blood smear when placed under a microscope oil microscope for continuous observation of 100 mature neutrophils. In this embodiment, the cytochemical staining evaluation module of CML-CP utilizes the deep learning algorithm fast-RCNN to classify and count the mature neutrophils with different continuous staining degrees in the peripheral blood smear stained by AKP, and the different staining degrees are assigned with corresponding integral values, and the positive degree of staining is reflected by the sum of the integral values (AKP integral).

The disease type judging module: the module analyzes the morphological parameters of the bone marrow of the case by using an artificial intelligence technology and judges the disease type of the case to be diagnosed. The characteristic extraction module extracts deep learning-based effective characteristics of case bone marrow morphology from case bone marrow morphology parameters according to the characteristic of blood system disease bone marrow morphology diagnosis in medical diagnosis professional knowledge, wherein the effective characteristics generally comprise bone marrow hyperplasia degree, disease characteristic nucleated cell proportion and disease characteristic cytochemical staining type positive degree. The morphological characteristics of the bone marrow of CML-CP are mainly shown in that the bone marrow hyperplasia is obviously extremely active, the proportion of primitive cells is less than 10%, the proportion of neutrophil intermediate stage cells (myeloblast, metagranulocyte, and rod-shaped granulocyte) is increased, the proportion of eosinophilic and basophilic granulocytes is increased, the ratio of granulocyte-erythrocyte is increased, and the integral of AKP is reduced, so that aiming at AI auxiliary diagnosis of CML-CP based on medical diagnosis professional knowledge, the extracted case characteristics are the bone marrow hyperplasia degree, the sum of primitive cells, neutrophil intermediate stage cells (myeloblast, metagranulocyte, and rod-shaped granulocyte), the ratio of eosinophilic granulocytes, the ratio of basophilic granulocytes, the ratio of granulocyte-erythrocyte and the integral of AKP.

The case classification module is based on a machine learning algorithm, combines medical diagnosis professional standards and clinical big data, learns the association between the bone marrow morphological effective characteristics and the blood disease types by applying a knowledge reasoning method, establishes a blood disease type classifier model based on the acquired association, and judges the disease types of cases by using the extracted case characteristics. According to clinical experience and case data, a knowledge reasoning method is used for learning the association between the bone marrow morphological effective characteristics and the CML-CP, a CML-CP recognition classifier model based on a Support Vector Machine (SVM) is established, a CML-CP case classification module is trained and verified in advance based on the SVM, the SVM is a two-classification model, a basic model of the CML-CP case classification module is a linear classifier defined on a characteristic space and has the largest interval, and a learning algorithm is an optimization algorithm for solving convex quadratic programming. The introduction of the kernel makes it a substantially non-linear classifier. The basic idea of the support vector machine is to solve for the separate hyperplane that can correctly partition the training data set and that has the largest geometrical separation. As shown in fig. 3, w · x + b is 0, i.e., a separation hyperplane, and there are an infinite number of such hyperplanes for a linearly separable data set, but the separation hyperplane with the largest geometric spacing is unique. The support vector machine is a nonlinear support vector machine with a Gaussian kernel. The training set and the verification set are composed of related parameters of the cases with confirmed previous bone marrow morphology, and the performance of the algorithm reaches an ideal level before the cases participate in case classification of the cases to be diagnosed. Solving convex quadratic programming problem by nonlinear support vector machine

0≤α_i≤C,i＝1,2,...,N

Get the optimal solution

Wherein the content of the first and second substances,is the kernel function, K (x, z) is phi (x) phi (z), which is a mapping from the input space to the feature space.

In conclusion, the blood system disease auxiliary diagnosis system applies the artificial intelligence technology to a whole set of flow of bone marrow morphological image recognition and disease analysis, can realize AI auxiliary diagnosis with high accuracy rate on blood system diseases, can improve the working efficiency and reduce the pressure of clinical examination work. The system combines medical knowledge with artificial intelligence technology, is different from the methods disclosed by the prior documents and patents, fuses medical diagnosis professional standards and clinical big data on the basis of analyzing the classification and proportion of marrow nucleated cells, increases the learning of two marrow morphological effective characteristics of myeloproliferative degree and cytochemical staining degree, evaluates each effective characteristic of marrow morphology of deep learning-based cases, more comprehensively covers indexes and parameters of marrow morphological diagnosis, and utilizes the artificial intelligence technology to learn and extract more effective, more accurate and more complex case characteristics according to the morphological characteristics of diseased marrow, so that the AI auxiliary diagnosis result has stronger scientificity and interpretability. In the full-flow AI-assisted diagnosis of CML-CP in the examples, the diagnosis accuracy reaches 98.75%. In the differential diagnosis of normal and various types of malignant disease cases of blood systems, the proportion of various nucleated cells is taken as the case characteristics, two new characteristics of myeloproliferation degree and AKP integral are increased, and the diagnosis accuracy can be improved from 90.38 percent to 98.85 percent.