CN113299392A - New coronary pneumonia CT sign identification and rapid diagnosis system - Google Patents
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Abstract
The invention relates to a new coronary pneumonia CT symptom identification and rapid diagnosis system, wherein an image acquisition unit is used for acquiring a CT image of new coronary pneumonia to be identified and detected and preprocessing the CT image of the new coronary pneumonia to be identified and detected to obtain a preprocessed CT image of the new coronary pneumonia to be identified and detected; the sign identification unit is used for carrying out forward identification and classification on the CT image of the new coronary pneumonia to be identified and detected; the diagnosis model unit is used for collecting classified sample CT images according to specified signs and obtaining a prediction model of a diagnosis result through back propagation neural network training by utilizing a sample data set; and the network service unit is used for storing the original CT image, the symbolic classification data and the model diagnosis result and sending the prediction result to the client. The invention can automatically identify the signs and carry out rapid diagnosis according to the identification result, thereby realizing case screening, reducing the workload and accelerating the efficiency of new crown detection and diagnosis.
Description
Technical Field
The invention relates to the technical field of image detection, in particular to a CT sign recognition and rapid diagnosis system for new coronary pneumonia.
Background
Since the outbreak of new coronary pneumonia, the new coronary pneumonia has rapidly spread to the world due to the characteristic of strong infectivity, and epidemic situation seriously threatens the society and the health and safety of people. In order to effectively prevent the spread of new crown pneumonia, the currently effective means is that once a suspected new crown case is found, the suspected new crown case should be detected and isolated for treatment as soon as possible.
The main means for detecting the new corona is nucleic acid detection and CT screening at present, while the supply of nucleic acid detection test paper in some countries is seriously insufficient, and false negative results may occur due to high specificity and low sensitivity of nucleic acid detection of the novel coronavirus. In the case of a large number of suspected cases and related contact cases, the imaging physician is also burdened with a large amount of work if a large-scale CT investigation is to be performed.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the CT sign recognition and rapid diagnosis system for the new coronary pneumonia can automatically recognize signs of CT images, rapidly diagnose according to recognition results, further realize case screening, and accelerate the efficiency of new coronary detection and diagnosis on the premise of reducing the workload of doctors.
The CT symptom identification and rapid diagnosis system for the new coronary pneumonia comprises an image acquisition unit, a symptom identification unit, a diagnosis model unit, a network service unit and a client, wherein the image acquisition unit comprises a CT machine and an image processing module and is used for acquiring a CT image of the new coronary pneumonia to be identified and detected and preprocessing the CT image of the new coronary pneumonia to be identified and detected to obtain a preprocessed CT image of the new coronary pneumonia to be identified and detected; the symptom identification unit is used for carrying out forward identification and classification on the CT image of the new coronary pneumonia to be identified and detected; the diagnosis model unit is used for collecting classified sample CT images according to specified signs and obtaining a prediction model of a diagnosis result through back propagation neural network training by utilizing a sample data set; the network service unit is used for storing original CT images, symbolic classification data and model diagnosis results and sending the prediction results to the client; the client is used for automatically receiving the model diagnosis result and actively accessing the network service unit; the CT machine is connected with the input end of the image processing module, the output end of the image processing module is connected with the input end of the symptom identification unit, the output end of the symptom identification unit is connected with the input end of the diagnosis model unit, the output ends of the image processing module, the symptom identification unit and the diagnosis model unit are all connected with the network service unit, and the network service unit is in wireless data communication with the client.
In order to classify, diagnose and identify each sign, the number of the diagnosis model units corresponds to the number of classification types of the sign identification units one by one.
In order to build a corresponding diagnostic prediction model from the data set, the diagnostic model unit comprises a BP neural network module.
In order to realize the bolting of the diagnosis result and the backtracking of the original CT image, the classification information and the diagnosis information, the network service unit comprises a wireless data transmission module, a cloud server and a data interface.
The operation method of the new coronary pneumonia CT sign identification and rapid diagnosis system comprises the following steps,
s11: the CT machine collects a CT image of the new coronary pneumonia to be identified and detected, and the image processing module preprocesses the CT image of the new coronary pneumonia to be identified and detected to obtain the preprocessed CT image of the new coronary pneumonia to be identified and detected;
s12: the sign identification unit carries out forward identification on the CT image to be identified and detected for the new coronary pneumonia and carries out sign classification;
s13: the diagnosis model unit receives the CT images of the classified samples according to the appointed signs correspondingly and obtains a corresponding diagnosis result by utilizing a prediction model constructed in advance;
s14: the cloud server respectively stores and stores the original CT image, the symbolic classification data and the model diagnosis result, and sends the prediction result to a client of a doctor by using the wireless data transmission module;
s15: and the doctor accesses the cloud server through the data interface according to the larger deviation value or the diagnosis confirmation result in the client, calls the original CT image and the symbolic classification data to perform rechecking or consultation.
The model establishing and rapid diagnosis method of the new coronary pneumonia CT sign identification and rapid diagnosis system comprises the following steps,
s21: collecting the diagnostic CT image data of the previously diagnosed new coronary pneumonia patient and screening out a sample construction data set corresponding to the sign identification;
s22: repeatedly training and iterating the sample data set by using a BP neural network module to obtain a prediction model of a diagnosis result;
s23: receiving the CT image of the new coronary pneumonia to be identified and detected after being classified by the symptom identification unit, and obtaining the corresponding diagnosis result based on the prediction model
Specifically, the symptom identification classification comprises a roxburgh rose symptom, a halo knot symptom, a cloudy catkin symptom, a snow and grey symptom, a gypsum symptom, a batwing symptom, a white lung symptom and a scabbard symptom.
Specifically, the index of the diagnosis result of the prediction model comprises radioactive inflammation, bacterial inflammation, tuberculosis, fibrosis, metastatic tumor, hemorrhage, edema and scleroderma lesion.
The invention relates to a new coronary pneumonia CT sign identification and rapid diagnosis system, which overcomes the defects of low efficiency and large workload of the existing large-range CT investigation, and has the specific beneficial effects that:
(1) the collected CT image can be automatically subjected to sign recognition and classified for subsequent processing;
(2) a prediction model can be established to carry out specific diagnosis on the image under the corresponding symptom;
(3) a large number of common results can be screened out, and the diagnosis result with confirmed diagnosis or large deviation value can be backtracked and confirmed;
and further, case screening is realized, and the efficiency of new crown detection and diagnosis is accelerated on the premise of reducing the workload of doctors.
Drawings
The invention further discloses a new coronary pneumonia CT sign identification and rapid diagnosis system with the following combination of the attached drawings:
FIG. 1 is a block diagram of the structural connection lines of the new coronary pneumonia CT sign identification and rapid diagnosis system;
FIG. 2 is a flow chart of the method of operation of the new coronary pneumonia CT sign identification and rapid diagnosis system;
fig. 3 is a flow chart illustrating the steps of the model building and rapid diagnosis method of the new coronary pneumonia CT sign identification and rapid diagnosis system.
In the figure:
1-an image acquisition unit; 11-CT machine, 12-image processing module;
2-a symptom identification unit;
3-a diagnostic model unit; 31-BP neural network module;
4-a network service unit; 41-wireless data transmission module, 42-cloud server and 43-data interface;
and 5, a client.
Detailed Description
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the description of the present invention, it is to be understood that the terms "left", "right", "front", "back", "top", "bottom", "inner", "outer", etc., indicate orientations and positional relationships based on those shown in the drawings, and are used only for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be considered as limiting the present invention.
The technical solution of the present invention is further described by the following specific examples, but the scope of the present invention is not limited to the following examples.
Example 1: as shown in fig. 1, the new coronary pneumonia CT symptom identification and rapid diagnosis system includes an image acquisition unit 1, a symptom identification unit 2, a diagnosis model unit 3, a network service unit 4, and a client 5, wherein the image acquisition unit 1 includes a CT machine 11 and an image processing module 12, and is configured to acquire a CT image of the new coronary pneumonia to be identified and detected, and preprocess the CT image of the new coronary pneumonia to be identified and detected, so as to obtain a preprocessed CT image of the new coronary pneumonia to be identified and detected; the symptom identification unit 2 is used for carrying out forward identification and classification on the CT image of the new coronary pneumonia to be identified and detected; the diagnosis model unit 3 is used for collecting classified sample CT images according to specified signs and obtaining a prediction model of a diagnosis result through back propagation neural network training by utilizing a sample data set; the network service unit 4 is used for storing original CT images, symbolic classification data and model diagnosis results and sending the prediction results to the client; the client 5 is used for automatically receiving the model diagnosis result and actively accessing the network service unit 4; the CT machine 11 is connected with an input end of an image processing module 12, an output end of the image processing module 12 is connected with an input end of a symptom identification unit 2, an output end of the symptom identification unit 2 is connected with an input end of a diagnosis model unit 3, output ends of the image processing module 12, the symptom identification unit 2 and the diagnosis model unit 3 are all connected with a network service unit 4, and the network service unit 4 is in wireless data communication with the client 5.
Example 2: in order to classify, diagnose and identify all signs, the number of the diagnosis model units 3 of the new coronary pneumonia CT sign identification and rapid diagnosis system is in one-to-one correspondence with the number of the classified types of the sign identification units 2. To build a corresponding diagnostic prediction model from the data set, the diagnostic model unit 3 comprises a BP neural network module 31. In order to realize the bolting of the diagnosis result and the backtracking of the original CT image, the classification information and the diagnosis information, the network service unit 4 includes a wireless data transmission module 41, a cloud server 42 and a data interface 43. The remaining structure and connection relationship are as described in embodiment 1, and the description is not repeated.
Embodiment 1: as shown in fig. 2, the operation method of the new coronary pneumonia CT sign identification and rapid diagnosis system comprises the following steps,
s11: the CT machine collects a CT image of the new coronary pneumonia to be identified and detected, and the image processing module preprocesses the CT image of the new coronary pneumonia to be identified and detected to obtain the preprocessed CT image of the new coronary pneumonia to be identified and detected;
s12: the sign identification unit carries out forward identification on the CT image to be identified and detected for the new coronary pneumonia and carries out sign classification;
s13: the diagnosis model unit receives the CT images of the classified samples according to the appointed signs correspondingly and obtains a corresponding diagnosis result by utilizing a prediction model constructed in advance;
s14: the cloud server respectively stores and stores the original CT image, the symbolic classification data and the model diagnosis result, and sends the prediction result to a client of a doctor by using the wireless data transmission module;
s15: and the doctor accesses the cloud server through the data interface according to the larger deviation value or the diagnosis confirmation result in the client, calls the original CT image and the symbolic classification data to perform rechecking or consultation.
Specifically, the symptom identification classification comprises a roxburgh rose symptom, a halo knot symptom, a cloudy catkin symptom, a snow and grey symptom, a gypsum symptom, a batwing symptom, a white lung symptom and a scabbard symptom.
Specifically, the index of the diagnosis result of the prediction model comprises radioactive inflammation, bacterial inflammation, tuberculosis, fibrosis, metastatic tumor, hemorrhage, edema and scleroderma lesion.
Embodiment 2: as shown in fig. 3, the model building and rapid diagnosis method of the new coronary pneumonia CT sign identification and rapid diagnosis system comprises the following steps,
s21: collecting the diagnostic CT image data of the previously diagnosed new coronary pneumonia patient and screening out a sample construction data set corresponding to the sign identification;
s22: repeatedly training and iterating the sample data set by using a BP neural network module to obtain a prediction model of a diagnosis result;
s23: and receiving the CT image of the new coronary pneumonia to be identified and detected after being classified by the symptom identification unit, and obtaining the corresponding diagnosis result based on the prediction model.
The remaining methods and steps are as described in embodiment 1 and will not be described repeatedly.
The new crown pneumonia CT sign recognition and rapid diagnosis system overcomes the defects of low efficiency and large workload of the existing large-range CT investigation, can automatically recognize signs of CT images, and rapidly diagnoses according to recognition results, so as to realize case screening, and accelerate the efficiency of new crown detection diagnosis on the premise of reducing the workload of doctors.
The foregoing description illustrates the principal features, rationale, and advantages of the invention. It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments or examples, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The foregoing embodiments or examples are therefore to be considered in all respects illustrative and not restrictive. The scope of the invention is indicated by the appended claims rather than by the foregoing description, and all changes that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (8)
1. A new coronary pneumonia CT sign identification and rapid diagnosis system is characterized in that: comprises an image acquisition unit (1), a symptom identification unit (2), a diagnosis model unit (3), a network service unit (4) and a client (5), wherein,
the image acquisition unit (1) comprises a CT machine (11) and an image processing module (12), and is used for acquiring a CT image of new coronary pneumonia to be identified and detected, preprocessing the CT image of the new coronary pneumonia to be identified and detected, and acquiring a preprocessed CT image of the new coronary pneumonia to be identified and detected;
the symptom identification unit (2) is used for carrying out forward identification and classification on the CT image to be identified and detected with the new coronary pneumonia;
the diagnosis model unit (3) is used for collecting classified sample CT images according to specified signs and obtaining a prediction model of a diagnosis result through back propagation neural network training by utilizing a sample data set;
the network service unit (4) is used for storing original CT images, symbolic classification data and model diagnosis results and sending the prediction results to the client;
the client (5) is used for automatically receiving the model diagnosis result and actively accessing the network service unit (4);
the CT machine (11) is connected with the input end of the image processing module (12), the output end of the image processing module (12) is connected with the input end of the symptom identification unit (2), the output end of the symptom identification unit (2) is connected with the input end of the diagnosis model unit (3), the output ends of the image processing module (12), the symptom identification unit (2) and the diagnosis model unit (3) are connected with the network service unit (4), and the network service unit (4) is in wireless data communication with the client (5).
2. The system for CT sign identification and rapid diagnosis of new coronary pneumonia according to claim 1, wherein: the number of the diagnosis model units (3) is in one-to-one correspondence with the number of the classification types of the symptom identification unit (2).
3. The system for CT sign identification and rapid diagnosis of new coronary pneumonia according to claim 2, wherein: the diagnostic model unit (3) comprises a BP neural network module (31).
4. The system for CT sign identification and rapid diagnosis of new coronary pneumonia according to claim 3, wherein: the network service unit (4) comprises a wireless data transmission module (41), a cloud server (42) and a data interface (43).
5. The system for CT sign identification and rapid diagnosis of new coronary pneumonia according to claims 1 to 4, wherein: the method of operating the system comprises the steps of,
s11: the CT machine collects a CT image of the new coronary pneumonia to be identified and detected, and the image processing module preprocesses the CT image of the new coronary pneumonia to be identified and detected to obtain the preprocessed CT image of the new coronary pneumonia to be identified and detected;
s12: the sign identification unit carries out forward identification on the CT image to be identified and detected for the new coronary pneumonia and carries out sign classification;
s13: the diagnosis model unit receives the CT images of the classified samples according to the appointed signs correspondingly and obtains a corresponding diagnosis result by utilizing a prediction model constructed in advance;
s14: the cloud server respectively stores and stores the original CT image, the symbolic classification data and the model diagnosis result, and sends the prediction result to a client of a doctor by using the wireless data transmission module;
s15: and the doctor accesses the cloud server through the data interface according to the larger deviation value or the diagnosis confirmation result in the client, calls the original CT image and the symbolic classification data to perform rechecking or consultation.
6. The system for CT sign identification and rapid diagnosis of new coronary pneumonia according to claim 5, wherein: the model building and rapid diagnosis method comprises the following steps,
s21: collecting the diagnostic CT image data of the previously diagnosed new coronary pneumonia patient and screening out a sample construction data set corresponding to the sign identification;
s22: repeatedly training and iterating the sample data set by using a BP neural network module to obtain a prediction model of a diagnosis result;
s23: and receiving the CT image of the new coronary pneumonia to be identified and detected after being classified by the symptom identification unit, and obtaining the corresponding diagnosis result based on the prediction model.
7. The system for CT sign identification and rapid diagnosis of new coronary pneumonia according to claim 6, wherein: the symptom identification classification comprises a roxburgh rose symptom, a halo knot symptom, a yunnan symptom, a snow and grey symptom, a gypsum symptom, a batwing symptom, a white lung symptom and a scabbard symptom.
8. The system for CT sign identification and rapid diagnosis of new coronary pneumonia according to claim 7, wherein: the index of the diagnostic result of the prediction model comprises radioactive inflammation, bacterial inflammation, tuberculosis, fibrosis, metastatic tumor, hemorrhage, edema and scleroderma lesion.
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