Method and device for identifying chronic obstructive pulmonary disease, electronic device and storage medium

Abstract

The present disclosure relates to a method and device for identifying chronic obstructive pulmonary disease, electronic equipment and storage medium, and relates to the technical field of identifying chronic obstructive pulmonary disease. The method for identifying chronic obstructive pulmonary disease comprises: acquiring a lung image to be processed and a preset identification model; projecting the lung image to be processed according to a plurality of angles set to obtain a plurality of projection images; and completing the identification of chronic obstructive pulmonary disease of the lung image to be processed based on the preset identification model and using the plurality of projection images. The present disclosure can realize the identification of chronic obstructive pulmonary disease.

Description

Technical Field

[0001] This disclosure relates to the field of COPD identification technology, and more particularly to a method and apparatus, electronic device and storage medium for COPD identification. Background Technology

[0002] Chronic obstructive pulmonary disease (COPD) is a common, complex, and heterogeneous respiratory disease. It is characterized primarily by persistent airflow limitation and respiratory symptoms associated with damage to the trachea and alveoli. The main causes of COPD are smoking and significant exposure to toxic particles and gases. According to GOLD 2022, COPD is among the top three causes of death worldwide. Epidemiological surveys indicate that over 3 million people worldwide died from COPD in 2010, accounting for 6% of all deaths that year. In 2015, the estimated number of people aged 20 and above diagnosed with COPD in China reached 99,900,000 (95% Confidence Interval (CI): 76.3–135.7). In my country, COPD diagnosis is not yet standardized, resulting in a high rate of missed diagnoses and missed opportunities for early detection and treatment. At present, it is of great significance to explore new methods for the early diagnosis and detection of COPD, such as exploring technologies for detecting and diagnosing COPD based on artificial intelligence. These will help with the early screening of COPD and benefit personal health management. Summary of the Invention

[0003] This disclosure presents a method and device for identifying COPD, as well as an electronic device and storage medium.

[0004] According to one aspect of this disclosure, a method for identifying COPD is provided, comprising:

[0005] Acquire the lung image to be processed and the preset recognition model;

[0006] The lung image to be processed is projected at multiple preset angles to obtain multiple projected images;

[0007] Based on the preset recognition model, COPD identification is completed using the multiple projected images to process the lung image to be processed.

[0008] Preferably, the method for projecting the lung image to be processed at multiple predetermined angles to obtain multiple projected images includes:

[0009] Obtain multiple set angles and projection criteria;

[0010] Based on multiple predefined angles, the lung image to be processed is projected according to the projection criteria to obtain multiple projected images.

[0011] Preferably, the method for identifying COPD in the lung image to be processed based on the preset recognition model and using the multiple projected images includes:

[0012] The multiple projected images are respectively input into the preset recognition model to obtain multiple first classification results;

[0013] The results of having COPD and not having COPD in the multiple first classification results are statistically analyzed to obtain the first statistical result of having COPD and the second statistical result of not having COPD.

[0014] If the first statistical result is greater than the second statistical result, then the patient is determined to have COPD; otherwise, the patient is determined not to have COPD.

[0015] Preferably, before projecting the lung image to be processed at multiple predetermined angles to obtain multiple projected images, the method further includes:

[0016] Perform lung parenchyma segmentation on the lung image to be processed to obtain a lung parenchyma image;

[0017] Based on the radiomics computational model, the preset radiomics features corresponding to the lung parenchyma image are calculated;

[0018] Based on the preset recognition model, COPD identification is completed by using the multiple projected images and the preset radiomics features to process the lung images to be processed.

[0019] Preferably, the method for identifying COPD in the lung image to be processed based on the preset recognition model, using the multiple projected images and the preset radiomics features, includes:

[0020] Obtain the preset convolutional neural network;

[0021] Using the preset convolutional neural network, feature extraction is performed on the multiple projected images to obtain multiple sets of convolutional features;

[0022] The multiple sets of convolutional features and the preset image omics features are fused to obtain recognition features;

[0023] Based on the preset recognition model, and using the recognition features, COPD identification is completed for the lung image to be processed.

[0024] Preferably, the method for fusing the multiple sets of convolutional features and the preset image omics features to obtain the recognition features includes:

[0025] The image omics features are fused with each of the multiple sets of convolutional features to obtain multiple sets of recognition features;

[0026] Based on the preset recognition model, the COPD identification of the lung image to be processed is completed by utilizing the multiple sets of recognition features.

[0027] Preferably, the method for identifying COPD in the lung image to be processed based on the preset recognition model and utilizing the multiple sets of recognition features includes:

[0028] The multiple sets of recognition features are input into the preset recognition model to obtain multiple second classification results.

[0029] The results of having COPD and not having COPD in the multiple second classification results were statistically analyzed to obtain the third statistical result of having COPD and the fourth statistical result of not having COPD.

[0030] If the third statistical result is greater than the fourth statistical result, then the patient is determined to have COPD; otherwise, the patient is determined not to have COPD.

[0031] According to one aspect of this disclosure, a COPD identification device is provided, comprising:

[0032] The acquisition unit is used to acquire the lung image to be processed and the preset recognition model;

[0033] The projection unit projects the lung image to be processed at multiple preset angles to obtain multiple projected images;

[0034] The recognition unit is used to identify COPD in the lung image to be processed based on the preset recognition model and using the multiple projected images.

[0035] According to one aspect of this disclosure, an electronic device is provided, comprising:

[0036] processor;

[0037] Memory used to store processor-executable instructions;

[0038] The processor is configured to execute the COPD identification method described above.

[0039] According to one aspect of this disclosure, a computer-readable storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement the above-described method for identifying COPD.

[0040] In this embodiment, the lung image to be processed is projected at multiple predetermined angles to obtain multiple projected images. Based on the preset recognition model, the multiple projected images are used to complete the COPD identification of the lung image to be processed. This addresses the problem that COPD diagnosis is not yet standardized, resulting in a high rate of missed diagnoses and thus missing the opportunity for early detection and treatment. Furthermore, exploring new methods for early diagnosis and detection of COPD is of great significance, such as exploring technologies for detecting and diagnosing COPD based on artificial intelligence methods. These methods can help with early screening of COPD and benefit individual health management.

[0041] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure.

[0042] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0043] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the specification, serve to illustrate the technical solutions of this disclosure.

[0044] Figure 1 A flowchart illustrating a method for identifying COPD according to an embodiment of the present disclosure is provided.

[0045] Figure 2 A deep learning-based classification model according to an embodiment of the present disclosure is shown;

[0046] Figure 3 A block diagram of a COPD identification device according to an embodiment of the present disclosure is shown;

[0047] Figure 4 This is a block diagram illustrating an electronic device 800 according to an exemplary embodiment;

[0048] Figure 5 This is a block diagram illustrating an electronic device 1900 according to an exemplary embodiment. Detailed Implementation

[0049] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0050] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0051] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0052] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0053] It is understood that the various method embodiments mentioned above in this disclosure can be combined with each other to form combined embodiments without violating the principle and logic. Due to space limitations, this disclosure will not elaborate further.

[0054] In addition, this disclosure also provides a COPD identification device, electronic device, computer-readable storage medium, and program, all of which can be used to implement any of the COPD identification methods provided in this disclosure. The corresponding technical solutions and descriptions are described in the corresponding descriptions in the method section and will not be repeated here.

[0055] Figure 1 A flowchart illustrating a method for identifying COPD according to an embodiment of the present disclosure is shown, as follows: Figure 1 As shown, the method for identifying COPD includes: Step S101: acquiring a lung image to be processed and a preset recognition model; Step S102: projecting the lung image to be processed from multiple preset angles to obtain multiple projected images; Step S103: based on the preset recognition model, using the multiple projected images, completing the COPD identification of the lung image to be processed. This addresses the problem that COPD diagnosis is not yet standardized, resulting in a high rate of missed diagnoses and thus missing the opportunity for early detection and treatment. Therefore, exploring new methods for early diagnosis and detection of COPD is of great significance, such as exploring technologies for detecting and diagnosing COPD based on artificial intelligence methods. These methods will contribute to early screening of COPD and benefit individual health management.

[0056] Step S101: Obtain the lung image to be processed and the preset recognition model.

[0057] In the embodiments of this disclosure and other possible embodiments, the lung image can be one or more of CT images, MR images, or DR images. For example, in the embodiments of this disclosure and other possible embodiments, a lung image to be processed is first acquired. This lung image can be slice data obtained from an imaging device, such as a CT scanner. The lung image to be processed can be a CT image. Alternatively, the lung image to be processed can also be an MRI lung image or a CT-PET lung image, etc., and those skilled in the art can select a suitable lung image as needed.

[0058] Furthermore, in the embodiments and other possible embodiments disclosed herein, the preset recognition model can be a machine learning-based classification model, such as one or more of Support Vector Machine (SVM), Multilayer Perceptron (MLP), Random Forest (RF), K-Nearest Neighbors (KNN), Logistic Regression (LR), Decision Tree (DT), Gradient Boosting (GB), and Linear Discriminant Analysis (LDA). The preset recognition model can also be a deep learning classification model, such as the ResNet network. See details below. Figure 2 Detailed explanation.

[0059] Figure 2 A deep learning-based classification model according to an embodiment of this disclosure is shown. Figure 2As shown, conv represents a convolutional layer, s represents stride, pool represents a pooling layer, avg pool represents an average pooling layer, fc represents a fully connected layer, COPD indicates a diagnosis of COPD, and HC indicates a diagnosis of a healthy person (not diagnosed with COPD). Specifically, the deep learning-based classification model includes four modules; each module is repeated twice, with shortcut connections between modules. Each module primarily contains convolutional layers (conv) with either 3×3 or 1×1 kernels. The network concludes with an average pooling layer (avg pool) and a fully connected layer (fc). The main difference between ResNet26 and ResNet50 is the number of modules: ResNet26 has (2, 2, 2, 2), while ResNet50 has (3, 4, 6, 3). The main difference between ResNet26 and ResNet26d lies in the downsampling operations within each module. The ResNet26 network has downsampling operations in each module. It has two pathways: the left pathway has three convolutional layers with kernels of 1×1, 3×3, and 1×1, and kernel strides of 2, 1, and 1, respectively; the right pathway has one convolutional layer with a 1×1 kernel and a kernel stride of 2. The main difference between ResNet26d and ResNet26 is that the left pathway's kernel strides are 1, 2, and 1. In this disclosure, transfer learning is an effective method for ResNet-26d to avoid overfitting. This study uses ResNet convolutional weights from a pre-trained ImageNet model and trains the fully connected layers and output layer using the dataset presented in this study.

[0060] Step S102: Project the lung image to be processed at multiple set angles to obtain multiple projected images.

[0061] In the embodiments of this disclosure and other possible embodiments, the set multiple angles can be at least one set angle, such as any angle corresponding to the cross-section, coronal plane, or sagittal plane of the lung image to be processed. That is, the lung image to be processed can be an image in the xyz plane, which can be projected according to the three set angles (angle in the x direction, angle in the y direction, and angle in the z direction) of the xyz plane to obtain multiple projected images (projected image of the cross-section, projected image of the coronal plane, and projected image of the sagittal plane). At the same time, this disclosure does not limit the set multiple angles. For example, the set multiple angles can also be angles of 30°, 45°, 60°, etc., in the xy plane, yz plane, x direction, y direction, and z direction of the xz plane.

[0062] In this disclosure, the method for projecting the lung image to be processed according to a set multiple angles to obtain multiple projected images includes: acquiring a set multiple angles and projection criteria; and projecting the lung image to be processed according to the set multiple angles and the projection criteria respectively to obtain multiple projected images.

[0063] In the embodiments of this disclosure and other possible embodiments, the projection criterion can be the maximum intensity projection (MIP) criterion or the minimum intensity projection criterion, or it can be a projection criterion for a certain density interval. For example, the lung image to be processed can be projected according to the maximum intensity projection criterion along the z-axis, taking the maximum value, to obtain the corresponding projected image. Simultaneously, the lung image to be processed can also be projected according to the maximum intensity projection criterion along the x-axis and y-axis, respectively, taking the maximum values, to obtain two corresponding projected images.

[0064] For example, the lung image to be processed is a CT image, and the CT image has three layers of xy images, respectively. Projecting the image by taking the maximum value along the z-axis yields the corresponding projected image.

[0065] For example, the lung image to be processed is a CT image, which has three layers of xy images, respectively. Projecting the image by taking the minimum value along the z-axis yields the corresponding projected image.

[0066] Meanwhile, this disclosure proposes a density interval projection criterion to extract a set density interval for projection, obtaining the projection corresponding to the density of interest, thereby improving COPD identification. Specifically, the method of projecting the lung image to be processed according to the density interval projection criterion from multiple set angles to obtain multiple projected images includes: selecting values ​​of the lung image to be processed within a set density interval according to the multiple set angles, and superimposing the values ​​within the set density interval to obtain multiple projected images.

[0067] For example, the lung image to be processed is a CT image, which has three layers of xy images, namely the first xy image. Second xy graph Third xy image Projecting along the z-axis within a defined density range [1,3] yields the corresponding projected image. Specifically, the 2 in the first row and first column of the projected image is obtained by adding the 1 in the first row and first column of the first xy image within the set density interval [1,3]. Since the 5 in the first row and first column of the third xy image is not within the set density interval [1,3], the 5 is not added to other values.

[0068] Step S103: Based on the preset recognition model, COPD recognition of the lung image to be processed is completed using the multiple projection images.

[0069] In this disclosure, the method for identifying COPD in a lung image to be processed based on the preset recognition model and using the multiple projected images includes: inputting the multiple projected images into the preset recognition model to obtain multiple first classification results; statistically analyzing the results of having COPD and not having COPD in the multiple first classification results to obtain a first statistical result of having COPD and a second statistical result of not having COPD; if the first statistical result is greater than the second statistical result, it is determined that the person has COPD; otherwise, it is determined that the person does not have COPD.

[0070] For example, in embodiments of this disclosure and other possible embodiments, three projection images (a cross-sectional projection image, a coronal projection image, and a sagittal projection image) are input respectively. Figure 2 In the preset identification model, the preset identification model inputs three corresponding first classification results; the results of having COPD and not having COPD in the three first classification results are statistically analyzed to obtain a first statistical result (2) of having COPD and a second statistical result (1) of not having COPD; at this time, if the first statistical result (2) is greater than the second statistical result (1), then it is determined that the person has COPD.

[0071] In the embodiments of this disclosure and other possible embodiments, before projecting the lung image to be processed at multiple angles to obtain multiple projected images, the method further includes: performing lung parenchyma segmentation on the lung image to be processed to obtain a lung parenchyma image; projecting the lung parenchyma image at multiple angles to obtain multiple projected images; and then, based on the preset recognition model, using the multiple projected images, completing the COPD recognition of the lung image to be processed.

[0072] In embodiments of this disclosure and other possible embodiments, a method for performing lung parenchyma segmentation on the lung image to be processed to obtain a lung parenchyma image includes: acquiring a lung segmentation model; and performing lung parenchyma segmentation on the lung image to be processed based on the lung segmentation model to obtain a lung parenchyma image. The segmentation model can be a lung segmentation model based on traditional segmentation algorithms or a lung segmentation model based on deep learning, such as a lung segmentation model based on U-Net or U-ResNet. The training methods for these models are techniques commonly used by those skilled in the art, and will not be described in detail here.

[0073] In this disclosure, before projecting the lung image to be processed at multiple angles to obtain multiple projected images, the method further includes: performing lung parenchyma segmentation on the lung image to be processed to obtain a lung parenchyma image; calculating preset radiomics features corresponding to the lung parenchyma image based on a radiomics computing model; and completing COPD identification of the lung image to be processed using the multiple projected images and the preset radiomics features based on the preset recognition model.

[0074] In embodiments of this disclosure and other possible embodiments, a method for performing lung parenchyma segmentation on the lung image to be processed to obtain a lung parenchyma image includes: acquiring a lung segmentation model; and performing lung parenchyma segmentation on the lung image to be processed based on the lung segmentation model to obtain a lung parenchyma image. The segmentation model can be a lung segmentation model based on traditional segmentation algorithms or a lung segmentation model based on deep learning, such as a lung segmentation model based on U-Net or U-ResNet. Meanwhile, the radiomics computation model mentioned is an existing radiomics computation model, which can be obtained from the website https: / / pyradiomics.readthedocs.io / en / latest / index.html; a detailed description of the radiomics feature extraction model is not provided here.

[0075] In this disclosure, a method for identifying COPD in a lung image to be processed, based on the preset recognition model and utilizing the multiple projected images and the preset radiomics features, includes: obtaining a preset convolutional neural network; using the preset convolutional neural network to extract features from the multiple projected images respectively to obtain multiple sets of convolutional features; fusing the multiple sets of convolutional features and the preset radiomics features to obtain recognition features; and using the recognition features based on the preset recognition model to identify COPD in the lung image to be processed.

[0076] In embodiments of this disclosure and other possible embodiments, the preset convolutional neural network may be selected. Figure 2The publicly available model extracts features from the multiple projected images to obtain multiple sets of convolutional features. Alternatively, the segmentation model proposed in the paper *Med3d: Transfer learning for 3D medical image analysis* (Chen, S., K. Ma and Y. Zheng) can be selected; however, only the encoding structure 3D ResNet10, 3D ResNet18, or 3D ResNet34 within the segmentation model needs to be used to extract the convolutional features of the lung parenchyma image. Then, the multiple sets of convolutional features and the preset radiomics features are fused to obtain recognition features. Based on one or more of the preset recognition models, such as Support Vector Machine (SVM), Multilayer Perceptron (MLP), Random Forest (RF), K-Nearest Neighbors (KNN), Logistic Regression (LR), Decision Tree (DT), Gradient Boosting (GB), and Linear Discriminant Analysis (LDA), the recognition features are used to complete the COPD identification of the lung image to be processed.

[0077] This disclosure proposes a method for fusing convolutional features and radiomics features to improve the accuracy of COPD identification. Specifically, the method for fusing the multiple sets of convolutional features and the preset radiomics features to obtain identification features includes: fusing the radiomics features with each set of convolutional features of the multiple sets of convolutional features to obtain multiple sets of identification features; and using the preset identification model to complete the COPD identification of the lung image to be processed.

[0078] In the embodiments of this disclosure and other possible embodiments, the method of fusing the radiomics features with each of the multiple sets of convolutional features to obtain multiple sets of recognition features includes: performing a concatenation operation on the radiomics features and each of the multiple sets of convolutional features to obtain multiple sets of recognition features.

[0079] In the embodiments of this disclosure and other possible embodiments, the radiomics features and the multiple convolutional features are both radiomics features and multiple convolutional features corresponding to the segmented lung parenchyma image.

[0080] For example, the multiple sets of convolutional features corresponding to multiple projection images (cross-sectional projection image, coronal projection image, sagittal projection image) of the lung image to be processed or multiple projection images (cross-sectional projection image, coronal projection image, sagittal projection image) of the lung parenchyma image after lung segmentation are respectively: the first set of convolutional features is [1, 2, 3], the second set of convolutional features is [4, 5, 6], the third set of convolutional features is [7, 8, 9], and the radiomics feature is [1, 1, 1]. The radiomics feature and each set of convolutional features of the multiple sets of convolutional features are concatenated to obtain multiple sets of recognition features: [1, 2, 3, 1, 1, 1], [4, 5, 6, 1, 1, 1], [7, 8, 9, 1, 1, 1]. Wherein, the radiomics feature and the multiple sets of convolutional features are the same radiomics feature and multiple sets of convolutional features corresponding to the same lung image to be processed.

[0081] In the embodiments and other possible embodiments of this disclosure, whether COPD identification of the lung image to be processed is completed based on the preset recognition model using the multiple projected images, or based on the preset recognition model using the multiple sets of recognition features respectively, it is necessary to first train the preset recognition model, and then complete the COPD identification of the lung image to be processed based on the trained preset recognition model. Meanwhile, the method for training the preset recognition model is a technical means commonly used by those skilled in the art, and will not be described in detail in this disclosure.

[0082] Based on the above, in this disclosure, the method for identifying COPD in the lung image to be processed by utilizing the multiple sets of identification features based on the preset identification model includes: inputting the multiple sets of identification features into the preset identification model to obtain multiple second classification results; statistically analyzing the results of having COPD and not having COPD in the multiple second classification results to obtain a third statistical result of having COPD and a fourth statistical result of not having COPD; if the third statistical result is greater than the fourth statistical result, it is determined that the person has COPD; otherwise, it is determined that the person does not have COPD.

[0083] In the embodiments of this disclosure and other possible embodiments, the multiple sets of convolutional features and preset radiomics features corresponding to the three projection images (transverse projection image, coronal projection image, and sagittal projection image) are respectively stitched together to obtain three sets of recognition features; the three sets of recognition features are input into a preset recognition model, and the preset recognition model is input with three corresponding second classification results; the results of having COPD and not having COPD in the three second classification results are statistically analyzed to obtain a third statistical result (2 results) of having COPD and a fourth statistical result (1 result) of not having COPD; if the third statistical result (2 results) is greater than the fourth statistical result (1 result), then it is determined that the person has COPD.

[0084] The entity executing the COPD identification method can be a COPD identification device. For example, the COPD identification method can be executed by a terminal device, a server, or other processing devices. The terminal device can be a user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, in-vehicle device, wearable device, etc. In some possible implementations, the COPD identification method can be implemented by a processor calling computer-readable instructions stored in memory.

[0085] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0086] Figure 3 A block diagram of a COPD identification device according to an embodiment of the present disclosure is shown, such as Figure 3 As shown, the COPD identification device includes: an acquisition unit 101 for acquiring a lung image to be processed and a preset identification model; a projection unit 102 for projecting the lung image to be processed at multiple preset angles to obtain multiple projected images; and an identification unit 103 for identifying COPD in the lung image to be processed based on the preset identification model and using the multiple projected images.

[0087] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0088] This disclosure also proposes a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the above-described method. The computer-readable storage medium may be a non-volatile computer-readable storage medium.

[0089] This disclosure also proposes an electronic device, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured as described above. The electronic device may be provided as a terminal, a server, or other type of device.

[0090] Figure 4 This is a block diagram illustrating an electronic device 800 according to an exemplary embodiment. For example, the electronic device 800 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, or other terminal.

[0091] Reference Figure 4 The electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0092] Processing component 802 typically controls the overall operation of electronic device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0093] Memory 804 is configured to store various types of data to support the operation of electronic device 800. Examples of this data include instructions for any application or method operating on electronic device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0094] Power supply component 806 provides power to various components of electronic device 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800.

[0095] Multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0096] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when electronic device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0097] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0098] Sensor assembly 814 includes one or more sensors for providing state assessments of various aspects of electronic device 800. For example, sensor assembly 814 can detect the on / off state of electronic device 800, the relative positioning of components such as the display and keypad of electronic device 800, changes in position of electronic device 800 or a component of electronic device 800, the presence or absence of user contact with electronic device 800, orientation or acceleration / deceleration of electronic device 800, and temperature changes of electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0099] Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. Electronic device 800 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0100] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0101] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 804 including computer program instructions that can be executed by a processor 820 of an electronic device 800 to perform the above-described method.

[0102] Figure 5 This is a block diagram illustrating an electronic device 1900 according to an exemplary embodiment. For example, the electronic device 1900 may be provided as a server. (Refer to...) Figure 5The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the methods described above.

[0103] Electronic device 1900 may also include a power supply component 1926 configured to perform power management of electronic device 1900, a wired or wireless network interface 1950 configured to connect electronic device 1900 to a network, and an input / output (I / O) interface 1958. Electronic device 1900 can operate on an operating system stored in memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.

[0104] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions that can be executed by a processing component 1922 of an electronic device 1900 to perform the above-described method.

[0105] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0106] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0107] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0108] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0109] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0110] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0111] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0112] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0113] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for identifying COPD, characterized in that, include: Acquire the lung image to be processed and the preset recognition model; Perform lung parenchyma segmentation on the lung image to be processed to obtain a lung parenchyma image; According to the projection criteria, the lung parenchyma image is projected at multiple angles to obtain multiple projected images; wherein, the projection criteria are configured as the maximum density projection criterion, the minimum density projection criterion, and the density interval projection criterion. Based on the preset recognition model, using the multiple projected images, COPD identification of the lung image to be processed is completed, including: inputting the multiple projected images into the preset recognition model to obtain multiple first classification results; statistically analyzing the results of having COPD and not having COPD in the multiple first classification results to obtain a first statistical result of having COPD and a second statistical result of not having COPD; if the first statistical result is greater than the second statistical result, it is determined that the patient has COPD; otherwise, it is determined that the patient does not have COPD; further including: calculating preset radiomics features corresponding to the lung parenchyma image based on a radiomics computing model; based on the preset recognition model, using the multiple projected images and the preset radiomics features, COPD identification of the lung image to be processed is completed, including: obtaining a preset convolutional neural network; using the preset convolutional neural network to extract features from the multiple projected images to obtain multiple sets of convolutional features; fusing the multiple sets of convolutional features and the preset radiomics features to obtain recognition features; based on the preset recognition model, using the recognition features, COPD identification of the lung image to be processed is completed.

2. The method for identifying COPD according to claim 1, characterized in that, The process of fusing the multiple sets of convolutional features and the preset image omics features to obtain recognition features includes: The image omics features are fused with each of the multiple sets of convolutional features to obtain multiple sets of recognition features; Based on the preset recognition model, the COPD identification of the lung image to be processed is completed by utilizing the multiple sets of recognition features.

3. The method for identifying COPD according to claim 2, characterized in that, The step of identifying COPD in the lung image to be processed based on the preset recognition model and utilizing the multiple sets of recognition features includes: The multiple sets of recognition features are respectively input into the preset recognition model to obtain multiple second classification results; The results of having COPD and not having COPD in the multiple second classification results were statistically analyzed to obtain the third statistical result of having COPD and the fourth statistical result of not having COPD. If the third statistical result is greater than the fourth statistical result, then the patient is determined to have COPD; otherwise, the patient is determined not to have COPD.

4. A COPD identification device, characterized in that, include: The acquisition unit is used to acquire the lung image to be processed and the preset recognition model; The projection unit is used to perform lung parenchyma segmentation on the lung image to be processed to obtain a lung parenchyma image; According to the projection criteria, the lung parenchyma image is projected at multiple angles to obtain multiple projected images; wherein, the projection criteria are configured as the maximum density projection criterion, the minimum density projection criterion, and the density interval projection criterion. The identification unit is used to identify COPD in the lung image to be processed based on the preset identification model and using the multiple projected images, including: inputting the multiple projected images into the preset identification model to obtain multiple first classification results; statistically analyzing the results of having COPD and not having COPD in the multiple first classification results to obtain a first statistical result of having COPD and a second statistical result of not having COPD; if the first statistical result is greater than the second statistical result, it is determined that the patient has COPD; otherwise, it is determined that the patient does not have COPD; and further includes: based on a radiomics computational model. The process involves calculating preset radiomics features corresponding to the lung parenchyma image; based on the preset recognition model, using the multiple projection images and the preset radiomics features, performing COPD identification on the lung image to be processed, including: obtaining a preset convolutional neural network; using the preset convolutional neural network to extract features from the multiple projection images respectively, obtaining multiple sets of convolutional features; fusing the multiple sets of convolutional features and the preset radiomics features to obtain recognition features; and based on the preset recognition model, using the recognition features, performing COPD identification on the lung image to be processed.

5. An electronic device, characterized in that, include: processor; A memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute the COPD identification method according to any one of claims 1 to 3.

6. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the COPD identification method according to any one of claims 1 to 3.

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