CN111192662A - Medical image processing method based on random forest algorithm and storage medium - Google Patents
Medical image processing method based on random forest algorithm and storage medium Download PDFInfo
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- CN111192662A CN111192662A CN202010009003.7A CN202010009003A CN111192662A CN 111192662 A CN111192662 A CN 111192662A CN 202010009003 A CN202010009003 A CN 202010009003A CN 111192662 A CN111192662 A CN 111192662A
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Abstract
The invention relates to a medical image processing method based on a random forest algorithm and a storage medium, comprising the following steps of 101, obtaining characteristic information P of a user, obtaining characteristic information Q of a sample bank symptom, and integrating the characteristic information P and the characteristic information Q to obtain characteristic information H; 102, constructing a random forest for matching the user symptoms, and training by combining with the characteristic information H to obtain a random forest model for matching the user symptoms; 103, acquiring users and symptoms needing to be matched, and integrating the users and symptoms needing to be matched according to the step 101 to obtain integrated characteristic information; 104, repeating the steps until obtaining a degree index set { I ] of all symptoms of the user and the sample library1、I2、…ImGet MAX { I }1、I2、…ImAnd MAX { I }1、I2、…ImThe symptoms corresponding toAnd pushing the matched symptom to the user. The invention can save time for the doctor.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a medical image processing method based on a random forest algorithm and a storage medium.
Background
The term Hospital (Hospital) is from Latin's original to "guest", because when the establishment is started, people are refuge, and rest rooms are provided, so that the coming people feel comfortable and have the intention of hospitalization. Later, it became increasingly a professional organization that satisfied human medical needs, provided medical services, and a service site for housing and treating patients.
Hospitals refer to medical institutions that carry out necessary medical examination, treatment measures, nursing techniques, reception services, rehabilitation equipment, treatment and transportation and the like for patients according to laws, regulations and industrial specifications, and mainly aim to save and support injuries.
The current hospital is always full of patients, doctors in many departments cannot deal with the consultation of a large number of patients, and some slight typical symptoms can be completely determined according to the image information which is diagnosed in the past, so that if the doctors see the symptoms one by one, the time is wasted.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a medical image processing method and a storage medium based on a random forest algorithm.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a medical image processing method based on a random forest algorithm and a storage medium, comprising the following steps:
step 101, acquiring characteristic information P of a user, acquiring characteristic information Q of a sample library symptom, and integrating the characteristic information P and the characteristic information Q to obtain characteristic information H;
102, constructing a random forest for matching the user symptoms, and training by combining with the characteristic information H to obtain a random forest model for matching the user symptoms;
103, acquiring users and symptoms needing to be matched, integrating the users and symptoms needing to be matched according to the step 101 to obtain integrated characteristic information, and inputting the integrated characteristic information into the random forest model to obtain a matching degree index I;
104, repeating the steps until obtaining a degree index set { I ] of all symptoms of the user and the sample library1、I2、…ImGet MAX { I }1、I2、…ImAnd MAX { I }1、I2、…ImAnd pushing the corresponding symptom as the matching symptom of the user to the user.
Further, the characteristic information P of the user in the above step 101 includes the user's age, sex, disease history, and the medical image information of this time of the user, which is the medical image information selected by the user for consultation.
Further, the characteristic information Q of the specimen library symptom in step 101 includes an incidence rate corresponding to the age group of the symptom, an incidence rate corresponding to the sex, and a medical image information set for which diagnosis has been made in the past.
The characteristic information H in step 101 includes an incidence rate corresponding to the age of the user, an incidence rate corresponding to the sex of the user, whether the user has a history of the disease, and a similarity between the medical image of the user and a previously diagnosed medical image information set of the symptom.
Further, the method for constructing the random forest model in the step 102 only includes the following steps:
step 501, randomly extracting M new self-help sample sets in a place-by-place manner by adopting a bootstrap method from the symptoms of the sample library, and constructing M classification regression trees according to the self-help sample sets;
step 502, defining the number of the feature information P as n, randomly extracting m features at each node of each tree, wherein m is less than or equal to n, and selecting the feature with the most classification capability from the m features to perform node splitting in a mode of calculating information gain;
step 503, growing each tree to the maximum extent without pruning;
and 504, forming a random forest by the generated M trees, generating a random forest model, matching the symptoms of the user by the random forest model in a voting mode, and matching the user with the symptoms if the voting mode is that the ratio of the number of successfully matched trees to M is not lower than a threshold value N.
Further, the method for calculating the information gain in step 502 is to calculate through an ID3 algorithm, and specifically includes the following steps:
if the sub-feature P in the feature information P divides the sample library symptom set T into j subsets of T1, T2 and … Tj, the information gain of the sub-feature P is
Wherein, the number of M symptom sets T is the same as the M value in the step 501, | TjL is the number of samples in the subset that belong to Tj,
freq(Cjt) samples of T belong to CjThe frequency of classes, s, is the number of classes of samples in T.
Further, the voting method in the step 504 is as follows:
if C is defined as the need to push the tag, then
Where M is the number of trees, I (—) is an indicative function,
c is a single tree hiAs a result of the classification of the class C,
is a tree hiIf the weight of C is greater than the threshold value H, the leaf node number represents a single tree HiAgreeing to match the user with the symptom.
Further, the method for calculating the similarity between the medical image of the user and the previously diagnosed medical image information set of the symptom comprises the steps of,
and calculating the similarity of the medical image and each medical image in the previously diagnosed medical image set of the symptom through OpenCV, and taking the maximum value as the similarity of the medical image of the user and the previously diagnosed medical image information set of the symptom, wherein the medical image set is selected by a doctor according to the typical medical images which are previously diagnosed.
The invention also proposes a computer-readable storage medium, in which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method for processing medical images based on random forest algorithms.
The invention has the beneficial effects that: by providing the medical image processing method and the storage medium based on the random forest algorithm, the invention can match the user with the corresponding symptoms through the random forest, and then the user can search the corresponding department according to the matching result to carry out registration diagnosis, thereby being very convenient and saving the time of doctors.
Drawings
Fig. 1 is a flow chart of a medical image processing method and a storage medium based on a random forest algorithm according to the present invention.
Detailed Description
The conception, the specific structure and the technical effects of the present invention will be clearly and completely described in conjunction with the embodiments and the accompanying drawings to fully understand the objects, the schemes and the effects of the present invention. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The same reference numbers will be used throughout the drawings to refer to the same or like parts.
With reference to fig. 1, a medical image processing method based on a random forest algorithm and a storage medium are provided, which include the following steps:
step 101, acquiring characteristic information P of a user, acquiring characteristic information Q of a sample library symptom, and integrating the characteristic information P and the characteristic information Q to obtain characteristic information H;
102, constructing a random forest for matching the user symptoms, and training by combining with the characteristic information H to obtain a random forest model for matching the user symptoms;
103, acquiring users and symptoms needing to be matched, integrating the users and symptoms needing to be matched according to the step 101 to obtain integrated characteristic information, and inputting the integrated characteristic information into the random forest model to obtain a matching degree index I;
104, repeating the steps until obtaining a degree index set { I ] of all symptoms of the user and the sample library1、I2、…ImGet MAX { I }1、I2、…ImAnd MAX { I }1、I2、…ImAnd pushing the corresponding symptom as the matching symptom of the user to the user.
In a preferred embodiment of the present invention, the characteristic information P of the user in step 101 includes the user's age, sex, disease history, and the current medical image information of the user, which is the medical image information selected by the user for consultation.
In a preferred embodiment of this embodiment, the characteristic information Q of the specimen library symptom in step 101 includes an incidence rate corresponding to the age group of the symptom, an incidence rate corresponding to the sex, and a medical image information set for which diagnosis has been made in the past.
In a preferred embodiment of this embodiment, the characteristic information H in step 101 includes an incidence of disease according to the age of the user, an incidence of disease according to the sex of the user, whether the user has a history of the disease, and a similarity between the medical image of the user and a previously diagnosed medical image information set of the symptom. The user information can be acquired through a man-machine interaction interface, so that the user can input the information by himself or herself, and other modes can be adopted, and the method is reasonable.
As a preferred embodiment of the present invention, the method for constructing the random forest model only in the step 102 includes the following steps:
step 501, randomly extracting M new self-help sample sets in a place-by-place manner by adopting a bootstrap method from a symptom sample library, and constructing M classification regression trees according to the self-help sample sets;
step 502, defining the number of the feature information P as n, randomly extracting m features at each node of each tree, wherein m is less than or equal to n, and selecting the feature with the most classification capability from the m features to perform node splitting in a mode of calculating information gain;
step 503, growing each tree to the maximum extent without pruning;
and 504, forming a random forest by the generated M trees, generating a random forest model, matching the symptoms of the user by the random forest model in a voting mode, and matching the user with the symptoms if the voting mode is that the ratio of the number of successfully matched trees to M is not lower than a threshold value N.
As a preferred embodiment of the present invention, the method for calculating the information gain in step 502 is to calculate by using an ID3 algorithm, and specifically includes the following steps:
if the sub-feature P in the feature information P divides the sample library symptom set T into j subsets of T1, T2 and … Tj, the information gain of the sub-feature P is
Wherein, the number of M symptom sets T is the same as the M value in the step 501, | TjL is the number of samples in the subset that belong to Tj,
freq(Cjt) samples of T belong to CjThe frequency of classes, s, is the number of classes of samples in T.
As a preferred embodiment of this solution, the voting method in the above step 504 is:
if C is defined as the need to push the tag, then
Where M is the number of trees, I (—) is an indicative function,
c is a single tree hiAs a result of the classification of the class C,
is a tree hiIf the weight of C is greater than the threshold value H, the leaf node number represents a single tree HiAgreeing to match the user with the symptom.
In a preferred embodiment of this aspect, the method for calculating the similarity between the current medical image of the user and the previously diagnosed medical image information set of the symptom includes,
and calculating the similarity of the medical image and each medical image in the previously diagnosed medical image set of the symptom through OpenCV, and taking the maximum value as the similarity of the medical image of the user and the previously diagnosed medical image information set of the symptom, wherein the medical image set is selected by a doctor according to the typical medical images which are previously diagnosed. After the corresponding symptoms are obtained, a related database can be preferably constructed, when the corresponding symptoms are obtained, the database can be automatically checked, departments corresponding to the corresponding symptoms are pushed to the user, and people with slightly low cultural degree can quickly find out the targeted place registration.
When the specific scheme is implemented, the user can obtain the matched symptoms only by inputting the relevant data of the user, and then the user finds the corresponding department according to the matched symptoms to diagnose.
The invention also proposes a computer-readable storage medium, in which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method for processing medical images based on random forest algorithms.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium and can implement the steps of the above-described method embodiments when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, etc. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media which may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
While the present invention has been described in considerable detail and with particular reference to a few illustrative embodiments thereof, it is not intended to be limited to any such details or embodiments or any particular embodiments, but it is to be construed as effectively covering the intended scope of the invention by providing a broad, potential interpretation of such claims in view of the prior art with reference to the appended claims. Furthermore, the foregoing describes the invention in terms of embodiments foreseen by the inventor for which an enabling description was available, notwithstanding that insubstantial modifications of the invention, not presently foreseen, may nonetheless represent equivalent modifications thereto.
The above description is only a preferred embodiment of the present invention, and the present invention is not limited to the above embodiment, and the present invention shall fall within the protection scope of the present invention as long as the technical effects of the present invention are achieved by the same means. The invention is capable of other modifications and variations in its technical solution and/or its implementation, within the scope of protection of the invention.
Claims (9)
1. A medical image processing method based on random forest algorithm and a storage medium are characterized by comprising the following steps:
step 101, acquiring characteristic information P of a user, acquiring characteristic information Q of a sample library symptom, and integrating the characteristic information P and the characteristic information Q to obtain characteristic information H;
102, constructing a random forest for matching the user symptoms, and training by combining with the characteristic information H to obtain a random forest model for matching the user symptoms;
103, acquiring users and symptoms needing to be matched, integrating the users and symptoms needing to be matched according to the step 101 to obtain integrated characteristic information, and inputting the integrated characteristic information into the random forest model to obtain a matching degree index I;
104, repeating the steps until obtaining a degree index set { I ] of all symptoms of the user and the sample library1、I2、...ImGet MAX { I }1、I2、...ImAnd MAX { I }1、I2、...ImAnd pushing the corresponding symptom as the matching symptom of the user to the user.
2. The medical image processing method and storage medium based on random forest algorithm according to claim 1, wherein the characteristic information P of the user in the step 101 comprises the user's age, sex, disease history and the medical image information of this time selected by the user for consultation.
3. The method for processing medical image based on random forest algorithm and storage medium as claimed in claim 2, wherein the characteristic information Q of the sample base symptom in step 101 comprises the disease incidence corresponding to age group of symptom, the disease incidence corresponding to gender and the medical image information set for previous diagnosis.
4. The medical image processing method and storage medium according to claim 3, wherein the characteristic information H in step 101 includes an incidence rate corresponding to the age of the user, an incidence rate corresponding to the sex of the user, whether the user has a history of the disease, and a similarity between the medical image of the user and a previously diagnosed medical image information set of the symptom.
5. A medical image processing method and storage medium based on random forest algorithm as claimed in claim 4, wherein the step 102 only comprises the following steps:
step 501, randomly extracting M new self-help sample sets in a place-by-place manner by adopting a bootstrap method from the symptoms of the sample library, and constructing M classification regression trees according to the self-help sample sets;
step 502, defining the number of the feature information P as n, randomly extracting m features at each node of each tree, wherein m is less than or equal to n, and selecting the feature with the most classification capability from the m features to perform node splitting in a mode of calculating information gain;
step 503, growing each tree to the maximum extent without pruning;
and 504, forming a random forest by the generated M trees, generating a random forest model, matching the symptoms of the user by the random forest model in a voting mode, and matching the user with the symptoms if the voting mode is that the ratio of the number of successfully matched trees to M is not lower than a threshold value N.
6. The medical image processing method and the storage medium based on the random forest algorithm according to the claim 5, wherein the method comprises the following steps: the method for calculating the information gain in step 502 is to calculate through an ID3 algorithm, and specifically includes the following steps:
if the sub-feature P in the feature information P divides the sample library symptom set T into j subsets of T1, T2
Wherein, the number of M symptom sets T is the same as the M value in the step 501, | TjL is the number of samples in the subset that belong to Tj,
freq(Cjt) samples of T belong to CjThe frequency of classes, s, is the number of classes of samples in T.
7. The medical image processing method and the storage medium based on the random forest algorithm according to claim 5, wherein the voting in the step 504 is performed by:
if C is defined as the need to push the tag, then
Where M is the number of trees, I (—) is an indicative function,
c is a single tree hiAs a result of the classification of the class C,
is a tree hiIf the weight of C is greater than the threshold value H, the leaf node number represents a single tree HiAgreeing to match the user with the symptom.
8. The medical image processing method and storage medium based on random forest algorithm according to claim 4, wherein the method for calculating the similarity between the medical image of the user and the previously diagnosed medical image information set of the symptom comprises,
and calculating the similarity of the medical image and each medical image in the previously diagnosed medical image set of the symptom through OpenCV, and taking the maximum value as the similarity of the medical image of the user and the previously diagnosed medical image information set of the symptom, wherein the medical image set is selected by a doctor according to the typical medical images which are previously diagnosed.
9. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 8.
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| CN111753790A (en) * | 2020-07-01 | 2020-10-09 | 武汉楚精灵医疗科技有限公司 | Video classification method based on random forest algorithm |
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| CN110442746A (en) * | 2019-07-01 | 2019-11-12 | 佛山科学技术学院 | A kind of intelligent music method for pushing and storage medium based on random forests algorithm |
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| CN110442746A (en) * | 2019-07-01 | 2019-11-12 | 佛山科学技术学院 | A kind of intelligent music method for pushing and storage medium based on random forests algorithm |
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| CN111753790A (en) * | 2020-07-01 | 2020-10-09 | 武汉楚精灵医疗科技有限公司 | Video classification method based on random forest algorithm |
| CN111753790B (en) * | 2020-07-01 | 2023-12-12 | 武汉楚精灵医疗科技有限公司 | Video classification method based on random forest algorithm |
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