Deep learning-based intelligent diagnostic methods, systems, and devices for cervical spine degeneration

Abstract

This application provides a deep learning-based intelligent diagnostic method, system, device, and computer-readable storage medium for cervical spine degeneration. The deep learning-based intelligent diagnostic method for cervical spine degeneration includes: acquiring lateral X-ray images of the cervical spine; inputting the lateral X-ray images of the cervical spine into a preset intelligent diagnostic model for cervical spine degeneration; and outputting diagnostic results for cervical spine degeneration. The diagnostic results for cervical spine degeneration include: normal, narrowing of the intervertebral space, changes in physiological curvature, and osteophytes. Changes in physiological curvature further include: normal physiological curvature, straightening of the physiological curvature, and reverse curvature. According to the embodiments of this application, cervical spine degeneration can be diagnosed more accurately.

Description

Technical Field

[0001] This application belongs to the field of cervical spine degeneration diagnosis, and in particular relates to a deep learning-based intelligent diagnostic method, system, device and computer-readable storage medium for cervical spine degeneration. Background Technology

[0002] Currently, the main technologies for diagnosing cervical degeneration are based on feature recognition modules that identify various CT image features of patients with current cervical degenerative diseases and visualize them in the form of text options.

[0003] However, this method, based on three-dimensional CT images, cannot accurately diagnose cervical degeneration.

[0004] Therefore, how to more accurately diagnose cervical degeneration is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] This application provides a method, system, device, and computer-readable storage medium for intelligent diagnosis of cervical spine degeneration based on deep learning, which can more accurately diagnose cervical spine degeneration.

[0006] In a first aspect, embodiments of this application provide a deep learning-based intelligent diagnostic method for cervical spine degeneration, comprising:

[0007] Obtain lateral X-ray images of the cervical spine;

[0008] Input the lateral X-ray image of the cervical spine into the preset intelligent diagnostic model for cervical degeneration, and output the diagnostic results of cervical degeneration.

[0009] The diagnostic results for cervical degeneration include: normal, narrowing of the intervertebral space, changes in physiological curvature, and osteophytes; changes in physiological curvature include: normal physiological curvature, straightening of the physiological curvature, and reverse curvature.

[0010] Optionally, the processing flow of the intelligent diagnostic model for cervical spine degeneration includes:

[0011] The lateral X-ray images of the cervical spine are passed through a 3x3 convolutional layer to capture local features of the image;

[0012] An average pooling layer is used to downsample the feature map after convolution.

[0013] Optionally, after downsampling the convolutional feature map using an average pooling layer, the method further includes:

[0014] In module stage2, the downsampling unit and the basic unit are stacked once each;

[0015] In module stage 3, downsampling units are stacked once and basic units are stacked three times to enhance the feature learning ability;

[0016] In module stage4, the downsampling unit and the basic unit are stacked once each.

[0017] Optional, also includes:

[0018] A 1x1 convolutional layer is used for cross-channel feature fusion and dimensionality reduction;

[0019] Global pooling layers reduce the spatial dimension of a feature map to a single value, capturing global information from the entire feature map.

[0020] The fully connected layer receives feature vectors from the global pooling layer and outputs a predicted value;

[0021] The 3x3 convolutional layer and the 1x1 convolutional layer respectively introduce the Mish activation function.

[0022] Optionally, acquire lateral cervical spine X-ray images, including:

[0023] The patient's head should be kept in a neutral position, with both eyes looking straight ahead and the chin slightly tucked in so that the midsagittal plane of the head and neck is parallel to the camera stand panel.

[0024] The patient should keep their shoulders down to avoid obscuring the cervical spine area;

[0025] Determine the center position of the cervical spine and ensure that the cervical spine is completely within the X-ray field;

[0026] Align the center line with the midpoint of the cervical spine to ensure clarity of the lateral cervical spine image;

[0027] Operate the X-ray imaging equipment to perform exposure and capture images, obtaining lateral X-ray images of the cervical spine;

[0028] Check whether the X-ray images taken are clear and whether the cervical spine structure is intact. Save the images after confirming that there are no errors.

[0029] Image processing to adjust the contrast and brightness of lateral cervical spine X-ray images.

[0030] Optionally, before inputting the lateral cervical spine X-ray images into the preset intelligent diagnostic model for cervical degeneration, the method further includes:

[0031] Obtain a dataset of lateral cervical spine X-ray images;

[0032] The locations and types of degeneration in lateral cervical spine X-ray images were labeled, and the labeling results were saved in a txt file.

[0033] After shuffling the order of the lateral cervical spine X-ray images and their corresponding txt text, they were divided into training set, validation set and test set in a 6:2:2 ratio.

[0034] A smart diagnostic model for cervical spine degeneration is obtained by training the model using the training set, validation set, and test set.

[0035] Optionally, during model training, the batch size can be set to 32.

[0036] The initial learning rate is set to 1e-4, with an additional learning rate decay strategy. Every 5000 iterations, the learning rate decays to 0.9 of the previous learning rate.

[0037] Set the optimizer to the Adam optimizer;

[0038] Set the loss function to DICEloss;

[0039] The system is set to perform a validation test on the training and validation sets every 1000 iterations. The early stopping method is used to determine the network training stopping time, and an intelligent diagnostic model for cervical spine degeneration is obtained.

[0040] Secondly, embodiments of this application provide a deep learning-based intelligent diagnostic system for cervical spine degeneration, comprising:

[0041] The image acquisition module is used to acquire lateral X-ray images of the cervical spine;

[0042] The cervical degeneration diagnosis module is used to input lateral X-ray images of the cervical spine into a preset intelligent diagnostic model for cervical degeneration and output the diagnostic results of cervical degeneration.

[0043] The diagnostic results for cervical degeneration include: normal, narrowing of the intervertebral space, changes in physiological curvature, and osteophytes; changes in physiological curvature include: normal physiological curvature, straightening of the physiological curvature, and reverse curvature.

[0044] Thirdly, embodiments of this application provide an electronic device, which includes: a processor and a memory storing computer program instructions;

[0045] When the processor executes the computer program instructions, it implements a deep learning-based intelligent diagnostic method for cervical degeneration.

[0046] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement a deep learning-based intelligent diagnostic method for cervical degeneration.

[0047] The deep learning-based intelligent diagnostic method, system, device, and computer-readable storage medium of this application can more accurately diagnose cervical degeneration.

[0048] This deep learning-based intelligent diagnostic method for cervical spine degeneration includes:

[0049] Obtain lateral X-ray images of the cervical spine;

[0050] Input the lateral X-ray image of the cervical spine into the preset intelligent diagnostic model for cervical degeneration, and output the diagnostic results of cervical degeneration.

[0051] The diagnostic results for cervical degeneration include: normal, narrowing of the intervertebral space, changes in physiological curvature, and osteophytes; changes in physiological curvature include: normal physiological curvature, straightening of the physiological curvature, and reverse curvature. Attached Figure Description

[0052] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0053] Figure 1 This is a flowchart illustrating a deep learning-based intelligent diagnostic method for cervical degeneration provided in one embodiment of this application.

[0054] Figure 2 This is a schematic diagram of cervical degeneration types provided in one embodiment of this application;

[0055] Figure 3 This is a schematic diagram of the network structure of an intelligent diagnostic model for cervical degeneration provided in one embodiment of this application;

[0056] Figure 4 This is a schematic diagram of the structure of a deep learning-based intelligent diagnostic system for cervical degeneration provided in one embodiment of this application;

[0057] Figure 5 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0058] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0059] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0060] To address the problems of existing technologies, embodiments of this application provide a deep learning-based intelligent diagnostic method, system, device, and computer-readable storage medium for cervical spine degeneration. The deep learning-based intelligent diagnostic method for cervical spine degeneration provided in this application embodiment will be described first below.

[0061] Figure 1 This illustration shows a flowchart of a deep learning-based intelligent diagnostic method for cervical degeneration provided in one embodiment of this application. Figure 1 As shown, this deep learning-based intelligent diagnostic method for cervical spine degeneration includes:

[0062] S101. Obtain lateral X-ray images of the cervical spine;

[0063] S102. Input the lateral X-ray image of the cervical spine into the preset intelligent diagnostic model for cervical degeneration, and output the diagnostic results of cervical degeneration.

[0064] The diagnostic results for cervical degeneration include: normal, narrowing of the intervertebral space, changes in physiological curvature, and osteophytes; changes in physiological curvature include: normal physiological curvature, straightening of the physiological curvature, and reverse curvature.

[0065] Figure 2 This is a schematic diagram of cervical degeneration types provided in one embodiment of this application;

[0066] Figure 3 This is a schematic diagram of the network structure of an intelligent diagnostic model for cervical degeneration provided in one embodiment of this application.

[0067] In one embodiment, the processing flow of the intelligent diagnostic model for cervical degeneration includes:

[0068] The lateral X-ray images of the cervical spine are passed through a 3x3 convolutional layer to capture local features of the image;

[0069] An average pooling layer is used to downsample the feature map after convolution.

[0070] In one embodiment, after downsampling the convolutional feature map using an average pooling layer, the method further includes:

[0071] In module stage2, the downsampling unit and the basic unit are stacked once each;

[0072] In module stage 3, downsampling units are stacked once and basic units are stacked three times to enhance the feature learning ability;

[0073] In module stage4, the downsampling unit and the basic unit are stacked once each.

[0074] In one embodiment, it also includes:

[0075] A 1x1 convolutional layer is used for cross-channel feature fusion and dimensionality reduction;

[0076] Global pooling layers reduce the spatial dimension of a feature map to a single value, capturing global information from the entire feature map.

[0077] The fully connected layer receives feature vectors from the global pooling layer and outputs a predicted value;

[0078] The 3x3 convolutional layer and the 1x1 convolutional layer respectively introduce the Mish activation function.

[0079] In one embodiment, acquiring a lateral X-ray image of the cervical spine includes:

[0080] The patient's head should be kept in a neutral position, with both eyes looking straight ahead and the chin slightly tucked in so that the midsagittal plane of the head and neck is parallel to the camera stand panel.

[0081] The patient should keep their shoulders down to avoid obscuring the cervical spine area;

[0082] Determine the center position of the cervical spine and ensure that the cervical spine is completely within the X-ray field;

[0083] Align the center line with the midpoint of the cervical spine to ensure clarity of the lateral cervical spine image;

[0084] Operate the X-ray imaging equipment to perform exposure and capture images, obtaining lateral X-ray images of the cervical spine;

[0085] Check whether the X-ray images taken are clear and whether the cervical spine structure is intact. Save the images after confirming that there are no errors.

[0086] Image processing to adjust the contrast and brightness of lateral cervical spine X-ray images.

[0087] In one embodiment, before inputting the lateral cervical spine X-ray image into a preset intelligent diagnostic model for cervical degeneration, the method further includes:

[0088] Obtain a dataset of lateral cervical spine X-ray images;

[0089] The locations and types of degeneration in lateral cervical spine X-ray images were labeled, and the labeling results were saved in a txt file.

[0090] After shuffling the order of the lateral cervical spine X-ray images and their corresponding txt text, they were divided into training set, validation set and test set in a 6:2:2 ratio.

[0091] A smart diagnostic model for cervical spine degeneration is obtained by training the model using the training set, validation set, and test set.

[0092] In one embodiment, during model training, the batch size is set to 32.

[0093] The initial learning rate is set to 1e-4, with an additional learning rate decay strategy. Every 5000 iterations, the learning rate decays to 0.9 of the previous learning rate.

[0094] Set the optimizer to the Adam optimizer;

[0095] Set the loss function to DICEloss;

[0096] The system is set to perform a validation test on the training and validation sets every 1000 iterations. The early stopping method is used to determine the network training stopping time, and an intelligent diagnostic model for cervical spine degeneration is obtained.

[0097] Figure 4 This is a schematic diagram of the structure of a deep learning-based intelligent diagnostic system for cervical degeneration provided in one embodiment of this application.

[0098] This deep learning-based intelligent diagnostic system for cervical spine degeneration includes:

[0099] Image acquisition module 401 is used to acquire lateral X-ray images of the cervical spine;

[0100] The cervical degeneration diagnosis module 402 is used to input lateral X-ray images of the cervical spine into a preset intelligent diagnosis model for cervical degeneration and output the diagnosis results of cervical degeneration.

[0101] The diagnostic results for cervical degeneration include: normal, narrowing of the intervertebral space, changes in physiological curvature, and osteophytes; changes in physiological curvature include: normal physiological curvature, straightening of the physiological curvature, and reverse curvature.

[0102] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown.

[0103] The electronic device may include a processor 501 and a memory 502 storing computer program instructions.

[0104] Specifically, the processor 501 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0105] Memory 502 may include mass storage for data or instructions. For example, and not limitingly, memory 502 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 502 may include removable or non-removable (or fixed) media. Where appropriate, memory 502 may be internal or external to an electronic device. In a particular embodiment, memory 502 may be a non-volatile solid-state memory.

[0106] In one embodiment, memory 502 may be read-only memory (ROM). In one embodiment, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0107] The processor 501 reads and executes computer program instructions stored in the memory 502 to implement any of the deep learning-based intelligent diagnostic methods for cervical degeneration in the above embodiments.

[0108] In one example, the electronic device may also include a communication interface 503 and a bus 510. Wherein, as... Figure 5 As shown, the processor 501, memory 502, and communication interface 503 are connected through bus 510 and complete communication with each other.

[0109] The communication interface 503 is mainly used to realize communication between various modules, systems, units and / or devices in the embodiments of this application.

[0110] Bus 510 includes hardware, software, or both, that couples components of an electronic device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 510 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0111] Furthermore, in conjunction with the deep learning-based intelligent diagnosis method for cervical degeneration described in the above embodiments, this application embodiment can provide a computer-readable storage medium for implementation. This computer-readable storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the deep learning-based intelligent diagnosis methods for cervical degeneration described in the above embodiments.

[0112] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0113] The functional modules shown in the above-described block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0114] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or systems. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0115] The aspects of this application have been described above with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to embodiments of this application. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing system to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing system, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0116] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A deep learning-based intelligent diagnostic method for cervical spine degeneration, characterized in that, include: Obtain lateral X-ray images of the cervical spine; The lateral cervical spine X-ray image is input into a pre-defined intelligent diagnostic model for cervical degeneration, which outputs diagnostic results for cervical degeneration. These results include: normal, narrowed intervertebral space, altered physiological curvature, and osteophytes. Altered physiological curvature includes: normal physiological curvature, straightening of the physiological curvature, and reverse curvature. The processing flow of the intelligent diagnostic model for cervical degeneration includes: passing the lateral cervical spine X-ray image through a 3x3 convolutional layer to capture local features; downsampling the convolutional feature map using an average pooling layer; stacking downsampling units and basic units once each in module stage 2; stacking downsampling units once and basic units three times in module stage 3 to enhance feature learning ability; and stacking downsampling units and basic units once each in module stage 4. Each basic unit contains an MDFA module, a GCSA module, and a 3x3 depthwise separable convolutional layer. The processing within the basic unit includes channel segmentation, batch normalization, ReLU, stitching, and channel shuffling. It also includes: a 1x1 convolutional layer for cross-channel feature fusion and dimensionality reduction; a global pooling layer to reduce the spatial dimension of the feature map to a single value to capture global information of the entire feature map; a fully connected layer that receives feature vectors from the global pooling layer and outputs a predicted value; wherein, the 3x3 convolutional layer and the 1x1 convolutional layer respectively introduce the Mish activation function.

2. The intelligent diagnostic method for cervical degeneration based on deep learning according to claim 1, characterized in that, Obtain lateral X-ray images of the cervical spine, including: The patient's head should be kept in a neutral position, with both eyes looking straight ahead and the chin slightly tucked in so that the midsagittal plane of the head and neck is parallel to the camera stand panel. The patient should keep their shoulders down to avoid obscuring the cervical spine area; Determine the center position of the cervical spine and ensure that the cervical spine is completely within the X-ray field; Align the center line with the midpoint of the cervical spine to ensure clarity of the lateral cervical spine image; Operate the X-ray imaging equipment to perform exposure and capture images, obtaining lateral X-ray images of the cervical spine; Check whether the X-ray images taken are clear and whether the cervical spine structure is intact. Save the images after confirming that there are no errors. Image processing to adjust the contrast and brightness of lateral cervical spine X-ray images.

3. The intelligent diagnostic method for cervical spine degeneration based on deep learning according to claim 1, characterized in that, Before inputting the lateral cervical spine X-ray images into the preset intelligent diagnostic model for cervical degeneration, the method also includes: Obtain a dataset of lateral cervical spine X-ray images; The locations and types of degeneration in lateral cervical spine X-ray images were labeled, and the labeling results were saved to a txt file. After shuffling the order of the lateral cervical spine X-ray images and their corresponding txt text, they were divided into training set, validation set and test set in a 6:2:2 ratio. A smart diagnostic model for cervical spine degeneration is obtained by training the model using the training set, validation set, and test set.

4. The intelligent diagnostic method for cervical degeneration based on deep learning according to claim 3, characterized in that, During model training, the batch size is set to 32. The initial learning rate is set to 1e-4, with an additional learning rate decay strategy. Every 5000 iterations, the learning rate decays to 0.9 of the previous learning rate. Set the optimizer to the Adam optimizer; Set the loss function to DICEloss; The system is set to perform a validation test on the training and validation sets every 1000 iterations. The early stopping method is used to determine the network training stopping time, and an intelligent diagnostic model for cervical spine degeneration is obtained.

5. A deep learning-based intelligent diagnostic system for cervical spine degeneration, characterized in that, The system includes: The image acquisition module is used to acquire lateral X-ray images of the cervical spine; The cervical degeneration diagnosis module is used to input lateral X-ray images of the cervical spine into a preset intelligent diagnostic model for cervical degeneration and output the diagnostic results of cervical degeneration. The diagnostic results for cervical spine degeneration include: normal, narrowing of the intervertebral space, changes in physiological curvature, and osteophytes. Changes in physiological curvature include: normal physiological curvature, straightening of the physiological curvature, and reverse curvature. The processing flow of the intelligent diagnostic model for cervical spine degeneration includes: passing the lateral X-ray image of the cervical spine through a 3x3 convolutional layer to capture the local features of the image; using an average pooling layer to downsample the convolutional feature map; in module stage2, the downsampling unit and the basic unit are stacked once each; in module stage3, the downsampling unit is stacked once and the basic unit is stacked three times to enhance the learning ability of the features; in module stage4, the downsampling unit and the basic unit are stacked once each; the basic unit contains an MDFA module, a GCSA module, and a 3x3 depthwise separable convolutional layer. The processing in the basic unit includes channel segmentation, batch normalization, ReLU, stitching, and channel shuffling. It also includes: a 1x1 convolutional layer for cross-channel feature fusion and dimensionality reduction; a global pooling layer to reduce the spatial dimension of the feature map to a single value to capture global information of the entire feature map; a fully connected layer that receives feature vectors from the global pooling layer and outputs a predicted value; wherein, the 3x3 convolutional layer and the 1x1 convolutional layer respectively introduce the Mish activation function.

6. An electronic device, characterized in that, The electronic device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the deep learning-based intelligent diagnostic method for cervical degeneration as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the deep learning-based intelligent diagnostic method for cervical degeneration as described in any one of claims 1-4.

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