110 results about "Disease category" patented technology

The invention discloses a special disease queue data capturing method and system based on an intelligent medical knowledge graph, and relates to the technical field of medical information, and the method comprises the following steps: S1, constructing a special disease intelligent medical knowledge graph which comprises a bidirectional mapping relation between standard terms of a single disease category and clinical actual corpora, clinical text data is accumulated in a mode of combining manual annotation and machine learning, and a domain exclusive knowledge base containing symptoms, diagnosis and examination indexes is formed. According to the special disease queue data capturing method and system provided by the invention, by constructing the special disease intelligent medical knowledge graph, bidirectional mapping of single disease specification terms and clinical actual corpora is realized, and the problem of insufficient semantic understanding when non-standardized clinical corpora are processed by a traditional method is effectively solved; the entity information in the unstructured medical data can be accurately extracted by utilizing a natural language processing model and an inference engine.

The invention discloses a three-dimensional positron emission tomography data image classification method based on multi-stage progressive feature extraction, and is applied to the technical field of Alzheimer's disease auxiliary diagnosis. The auxiliary diagnosis method comprises the following steps: acquiring and preprocessing PET image data of an Alzheimer's disease patient; improving the reliability of the data set by using data enhancement; performing long-range dynamic modeling on the three-dimensional voxel sequence through a stacked Lmamba block; global context semantic adaptive fusion is realized through a layer-by-layer cross-scale channel attention fusion module (CSACF), and a channel spatial perception module (CSPM) is constructed to optimize spatial feature fusion; an inverted bottleneck module is mixed with long-distance space and position information to enhance the capturing capability of the model on detail features; and finally, predicting the disease category probability through global average pooling, full connection and softmax functions. According to the method, the precision of AD early diagnosis and MCI conversion risk prediction can be greatly improved, the defects of a medical image diagnosis method of a convolutional neural network (CNN) and Transform in long-range dependence on modeling and calculation complexity are overcome, and the method has good application prospects and is suitable for AD early detection and MCI conversion risk assessment.

The invention provides an apple disease detection method based on a Transform framework. The apple disease detection method comprises the following steps: firstly, making an apple disease detection image data set through apple disease image acquisition, apple disease image annotation and apple disease image annotation file division; then, an efficient multi-scale attention mechanism (EMA) is used and integrated with a backbone network, and a new feature extraction module (Basic Block EMA), a new adaptive feature extraction module (LAE) and a new re-parameterized CSP efficient layer aggregation module (RepNCSPELAN4) are designed to optimize an RT-DETR target detection model; inputting the obtained apple disease data set image into the optimized RT-DETR model for training verification, and obtaining an optimal model weight; and finally, importing the optimal weight model into an apple disease detection system, pre-processing the collected apple disease image, inputting the pre-processed apple disease image into the model for reasoning, and outputting a detection result containing disease category and position information, thereby realizing automatic and accurate detection of apple diseases. The invention aims to reduce the huge parameter quantity and model volume of an original network model by improving the RT-DETR model, and improve the detection efficiency and identification precision of the apple disease target.

The invention relates to the technical field of road detection, in particular to an intelligent road disease detection method and system based on a three-dimensional ground penetrating radar, and the method comprises the steps: obtaining and processing three-dimensional radar data, and obtaining a horizontal section image in the depth direction and vertical section images of a plurality of channels in the measuring line direction; determining the boundary position of the underground structure layer; selecting a horizontal slice image with a corresponding depth according to the boundary position, and inputting the horizontal slice image into a first disease detection model to obtain a preliminary disease candidate area; mapping the preliminary candidate region to a vertical profile image, intercepting a local vertical profile image of each channel, and inputting the local vertical profile image into a second disease detection model to obtain disease category information; fusion decision is carried out based on the transverse position of the candidate area and the disease category information of each channel, a final disease type is determined and is associated to the candidate area, consistency of disease positioning and type judgment is realized through cooperative utilization of the horizontal section image and the multi-channel vertical section image, and the accuracy and stability of detection are improved.

The invention relates to the technical field of medical information, and discloses a multi-dimensional verification medical record home page data real-time quality control and coding method, which comprises the following steps: S1, acquiring medical record home page data from a hospital information system or an electronic medical record system, the medical record home page data comprising structured data, unstructured data and time series data; and S2, classifying the home page data of the medical record according to a disease category classification standard, dividing the home page data of the medical record into different disease categories, and constructing a multi-dimensional tensor model related to the disease category for each category for subsequent data processing and feature extraction. By introducing an incremental relation updating and graph embedding mechanism, dynamic optimization and automatic expansion of the knowledge graph are realized, the problems of updating lag, limited expansion and out-of-control structure under high-frequency data of a traditional coding system are solved, and the real-time performance and the intelligent level of medical record home page coding and quality control are remarkably improved.

The invention relates to a cable tunnel disease detection method and device, computer equipment and a readable storage medium. The method comprises the following steps: acquiring a plurality of cable tunnel images of a cable tunnel acquired in a target time period, and acquiring a plurality of sensor data of the cable tunnel in the target time period; performing disease category identification on the plurality of cable tunnel images based on a cable tunnel identification model to obtain a disease category corresponding to each cable tunnel image output by the cable tunnel identification model; converting the disease categories corresponding to the plurality of cable tunnel images into a first matrix; converting the plurality of sensor data into a second matrix; splicing the first matrix and the second matrix to obtain a plurality of data sets; and fitting the plurality of data sets, and obtaining the cable tunnel disease grade according to a fitting result. By adopting the method, the detection efficiency and accuracy can be improved.

The invention discloses an unmanned aerial vehicle road disease detection and analysis system and method based on a YOLO algorithm, and the method comprises the following steps: synchronously collecting a road image and geographic coordinates through an unmanned aerial vehicle, and constructing an image data set with geographic reference; constructing an initial YOLO target detection model based on the data set; introducing an additional detection layer for detecting a pixel area into the model, and embedding a channel attention mechanism into the feature extraction network to obtain an improved YOLO target detection model; reasoning a data set by using the model, and outputting a disease category, a bounding box coordinate and a confidence coefficient; pixel coordinates are converted into geographic coordinates through a coordinate conversion algorithm, and disease records with accurate geographic coordinates are generated; and optimizing and updating the model based on the record as an incremental training sample. According to the method, the disease detection precision and the small target identification capability are effectively improved, and the long-term adaptability and generalization performance of the model are enhanced through a closed-loop incremental learning mechanism.

The embodiment of the invention provides a disease dynamic prediction method and device based on multi-source factors, a medium and a program product, and relates to the field of intelligent medical treatment. Through adaptive segmentation normalization and multi-source feature fusion technologies, the heterogeneity problem of multi-source physiological data is effectively solved, and the robustness of feature expression is remarkably improved while key physiological events are reserved; according to the dynamic prediction model based on the space-time-disease association tensor and the disease collaborative gating, explicit modeling of the three-dimensional relationship of the disease category, the physiological index and the time step is realized for the first time, so that a specific physiological index time sequence mode dependent on diseases such as arrhythmia and the like is accurately captured; the accuracy of multi-label prediction is greatly improved through coding disease co-occurrence prior; in combination with a dynamic width full-connection layer and a gradient-driven adaptive optimization strategy, the model can automatically adjust a network structure and training parameters according to the complexity of input features, and the accuracy is improved.

The invention discloses a medical insurance payment-oriented disease type cost benchmark model establishment method and application. The method comprises the steps of collecting medical insurance or hospital historical disease type settlement data; performing standardization processing on historical disease category settlement data, and performing data statistics on charging items according to disease categories; establishing a disease cost benchmark model, and calculating benchmark frequency and benchmark number of the disease by using historical disease settlement data; adjusting the obtained benchmark frequency and benchmark number by using a large language model to obtain recommended benchmark frequency and benchmark number; in combination with diagnosis and treatment specifications of professional clinical personnel and clinical habits of an overall planning area, the recommended benchmark frequency and benchmark number are manually adjusted to obtain disease benchmark values, and the disease benchmark values are added into a disease data set. Through the constructed case cost benchmark model, disease cost standardization management is promoted through benchmark management, diagnosis and treatment behavior standardization and medical insurance payment cost management and control are achieved, and the balance between compliance medical treatment and economical medical treatment is achieved.

The invention provides a space disease physiotherapy system fusing multi-source heterogeneous biological information, and relates to the technical field of intelligent spaceflight physiotherapy. The system comprises a broadcast module, a first acquisition module, a second acquisition module, a category determination module and a physiotherapy scheme module. The broadcasting module is used for generating and broadcasting space disease diagnosis problems according to a preset symptom description list, the first acquisition module and the second acquisition module are used for acquiring answer voice information and physiological index data, the category determination module is used for determining category information of space diseases suffered by astronauts, and the physiotherapy scheme module is used for determining physiotherapy schemes. According to the method and the device, accurate space disease screening prediction can be carried out on the astronaut according to the collected answer voice and the physiological indexes without guidance of professional physicians, dependence on the professional physicians is reduced, the accuracy and the efficiency of screening prediction of the space disease suffered by the astronaut are improved, effective support is provided for further determination of a physical therapy scheme, and the method and the device are suitable for popularization and application. And the physical therapy effect of space diseases is improved.

The invention belongs to the technical field of underground pipeline detection, and discloses an underground pipeline CCTV camera auxiliary control method and device based on deep learning, and the method comprises the steps: firstly obtaining continuous real-time image frames, and obtaining the results of a disease type, detection confidence, a detection frame position and the like through a pipeline disease object detection model; starting tracking after a disease is detected for the first time, associating subsequent frame results according to a category consistency and position similarity rule, and constructing a continuous frame detection result set; based on the set, time stability and space stability are judged in sequence, and control-level confirmable diseases are screened out; and calculating an effective shooting angle interval by combining the transverse coordinate distribution range of the detection frame and the field angle parameter of the camera, and finally controlling the camera to execute rotary shooting in the interval. The method can reduce the false triggering rate, improve the disease imaging quality and detection efficiency, reduce the manual dependence and guarantee the reliability of the detection result.

The present application relates to the technical field of image processing, in particular to a kind of agricultural disease detection method based on multi-scale feature extraction, comprising: obtaining target disease image, according to the mode of ladder convolution processing, the hierarchical branch of target disease image is constructed, under any hierarchical branch, with the mode of each level series coupling, determine the local disease feature of target disease image;Local disease feature is extracted by channel attention, and the local disease feature after attention recalibration is obtained;Local disease feature is mapped into semantic sequence space, and local disease feature is spatially attenuated and weighted to determine the disease distribution characteristics of crop disease under multi-scale fusion;At least one disease category is obtained based on the spatial position corresponding to disease distribution characteristics, and the average time and average accuracy of disease detection are combined to determine the disease detection effect.The precision and efficiency of disease identification are improved.

The invention relates to the technical field of skin disease classification, in particular to an auxiliary skin disease classification method and system based on multi-modal medical data, and the method comprises the steps: extracting features of a plurality of types of first data, and obtaining a plurality of initial feature vectors; performing low-level fusion and high-level fusion according to the plurality of initial feature vectors to obtain a comprehensive feature vector, inputting the comprehensive feature vector into a classifier, and determining a predicted skin disease category; and generating a diagnosis report according to the predicted skin disease category. According to the invention, low-level fusion and high-level fusion are carried out according to the plurality of initial feature vectors to obtain the comprehensive feature vector, and the predicted skin disease category is determined by fusing the data of the plurality of categories of the skin diseases and inputting the comprehensive feature vector into the classifier, so that the accuracy of skin disease classification is improved.

The present disclosure relates to a data model establishing method and device, and a clinical auxiliary decision method and device. The data model establishing method comprises: field disassembling historical data according to different disease categories; structured processing is performed according to the fields obtained by disassembling to form a structured output rule; and a disease data model is established according to the structured output rule combined with clinical data. The present disclosure also relates to a data model establishing device, an electronic device and a computer readable medium, and a clinical auxiliary decision method, device, electronic device and computer readable medium based on the data model establishing method. The structured output rule is generated by structured processing of diseases, and the clinical data is saved in a relational structure in a database according to the rule to form a data model, so that the clinical data can be effectively utilized, the interconnection and intercommunication between data can be realized, and the research conversion rate can be improved.

The application discloses an information processing method and device for medical image classification, equipment and medium, relates to the technical field of image recognition, and comprises the following steps: dividing a plurality of preset disease categories into a preset head category and a preset tail category; clustering each preset disease category in the preset tail category into a plurality of abnormal disease categories, and determining third training data corresponding to each abnormal disease category based on second training data corresponding to each preset disease category in each abnormal disease category; determining a target data set based on the first training data, the preset disease category corresponding to the first training data, the third training data, and the abnormal disease category corresponding to the third training data; training an initial classification model based on the target data set to obtain a target classification model, and generating a target disease classification result of an initial medical image to be recognized by using the target classification model. The application can efficiently process long-tail distribution data of medical images, and improve the recognition accuracy and reliability of rare diseases.

The invention provides an expressway foreground image disease detection method and system, and relates to the technical field of expressway disease detection, and the method comprises the steps: obtaining an expressway foreground image, carrying out the recognition according to a preset neural network model to obtain a disease region and a disease type, and generating a Grad-CAM thermodynamic diagram for the disease region through a Grad-CAM technology; performing grid division on the Grad-CAM thermodynamic diagram containing the disease area to obtain standard detection frames, and counting the proportion of abnormal pixels in each standard detection frame; judging whether the abnormal pixel proportion exceeds an abnormal threshold value or not; if yes, the disease area in the standard detection frame is an effective disease area, the accurate boundary of the effective disease area is drawn to quantify the disease size, the disease position and the disease number of the expressway foreground image are obtained according to the accurate boundary, and disease detection is achieved in combination with the disease category. According to the invention, the detection convenience, the identification precision and the size standardization are improved.

The invention relates to the technical field of rail disease detection, in particular to an urban rail multi-category disease real-time detection method based on improved YOLO v8, which comprises the following steps: constructing a multi-category disease image data set of an urban rail; yOLO v8 is used as a basic network framework, a feature reconstruction module is added in a detection head of a YOLO v8 network, and a disease detection model is constructed; the feature reconstruction module is used for optimizing normal data and disease data by adopting different loss functions, and carrying out lossless reconstruction of normal characterization and directional deduction and strong feature reconstruction of disease characterization relative to a normal characterization clustering center; training a disease detection model based on the multi-category disease image data set; and reasoning the real-time orbit data based on the trained disease detection model. According to the invention, high-precision and high-efficiency identification of urban rail disease categories can be realized.

The invention discloses a disease identification method, device and system, and a storage medium, and the method comprises the steps: inputting a to-be-identified disease image, and sequentially extracting the deep and shallow features of the to-be-identified disease image through a multi-layer feature extraction module; generating hierarchical prototypes of corresponding categories in different depth feature spaces, and fusing the hierarchical prototypes to form a comprehensive prototype; performing prototype classification reasoning based on the comprehensive prototype and a learnable measurement mechanism, and outputting disease categories; in the training process, the distinction degree between prototypes is enhanced by introducing prototype separation constraints; in the incremental learning stage, an original prototype of a new category sample is finely adjusted by adopting an elastic prototype updating strategy, a category prototype is newly added by utilizing a prototype classifier expansion strategy, and the old category recognition performance is ensured not to be lost. By adopting the technical scheme of the invention, similar category confusion can be effectively inhibited, and the accuracy and robustness of disease identification under a small sample condition are improved.

The invention relates to the technical field of medical informatization, and discloses a hospital DRG and DIP management information system based on artificial intelligence, which comprises an intelligent grouping module, a payment rate calculation module, a performance evaluation module and a data security module. The intelligent grouping module analyzes medical records, medication and inspection data by using a DeepSeek algorithm, automatically completes DRG / DIP grouping and generates a medical record combination report; the payment rate calculation module predicts the proportion of cost composition and payment through machine learning based on historical cost and settlement information, and automatically generates a payment standard capable of being dynamically adjusted; the performance evaluation module evaluates the efficiency and quality of departments or disease categories in a multi-dimensional manner, and provides a visual report to assist resource configuration optimization; the data security module guarantees the security of diagnosis and treatment data by adopting an encryption technology and authority management. The system is designed in a modularized mode, and data can be extracted from the HIS and the LIS and integrated to a unified platform. The method has the advantage that intelligent analysis and management of diagnosis and treatment data are realized by using a deep learning algorithm.

The present application relates to the technical field of computer vision and artificial intelligence, and particularly discloses a neural network optimization method based on a visual state space model for bridge diseases, which comprises the following steps: constructing a neural network comprising a feature extraction backbone network and a task adaptation head network, modeling the image feature sequence based on the visual state space model to capture local details and long-distance global dependencies, and using the task adaptation head network to output disease categories, confidence and positions; training the model using a standardized data set, dynamically optimizing by evaluating neuron importance, sorting and cutting redundant neurons; inputting the preprocessed image to be detected into the optimized model, performing feature extraction, multi-branch processing and non-maximum suppression, and outputting accurate disease recognition results. The present application improves disease feature extraction accuracy, realizes model lightweight, adapts to multi-scale disease detection, enhances the practicality of recognition results, and is suitable for intelligent bridge disease detection scenarios.

The present disclosure provides biomarker screening model training method and device, method and device, network, equipment and medium, and relates to the technical field of image processing. The implementation scheme of the present disclosure is: a heterogeneous graph construction module, configured to convert an obtained target data set into a heterogeneous graph structure; a double-flow graph convolution network module, wherein a protein interaction flow network is configured to output a first graph-level feature based on a first adjacency matrix; a gene regulation flow network is configured to output a second graph-level feature based on a second adjacency matrix; an attention fusion classification module is configured to weight and fuse the first graph-level feature and the second graph-level feature, and input the fused feature into a classifier to obtain a prediction probability value for a target disease category; and an explainable attribution module is configured to integrate a gradient along a path from a baseline input to an actual input, quantify a contribution score of each protein node feature in the heterogeneous graph structure to the prediction probability value, and output a candidate biomarker combination according to the contribution score.

The application discloses a kind of light-weight model and attention mechanism pest detection model and training method, belong to deep learning technical field.The model is based on YOLOv8-Seg architecture, by introducing light-weight MobileViT main network instead of original CSPDarknet53, combined with global feature fusion module and coordinate attention mechanism, while reducing parameter quantity and computational complexity, the feature expression ability is enhanced;In neck network, replace the original FPN structure with bidirectional feature pyramid network, fully excavate multi-scale feature and context information, optimize multi-scale feature fusion.Model combines the segmentation ability of SAM2 with the target detection ability of YOLO-SEG, shape adaptive labeling is realized, and the influence of complex background on detection accuracy is effectively shielded.mAP@[0.5:0.95] is stabilized at 0.8-0.9, and the balance of each disease category precision and recall rate index is good.

Quantum entanglement neuro morphology bridge disease holographic monitoring repair method and system: deploy quantum entanglement sensor array to obtain micro-disease information and fuse infrared sensor, laser radar, industrial camera data, encrypt transmission preprocessing through quantum communication network to generate fusion feature vector, disease category probability, severity score and comprehensive risk index; build bridge three-dimensional coordinate system to reconstruct three-dimensional geometric model, map the index to three-dimensional coordinate system, output three-dimensional disease holographic model through spatial clustering; weighted sum of comprehensive risk index, average severity, structure and traffic importance weight to get repair priority score and sort, under the constraint of cost and construction period, take repair cost, time, risk reduction and life increment as the comprehensive optimization objective, solve the construction control parameter and dynamically evaluate the repair quality to generate structured repair record; continuously collect monitoring data to calculate health indicators, establish degradation model to determine failure probability and remaining life, form maintenance plan and update parameters.

The present application relates to the technical field of pathological image classification, in particular to an intestinal mucosa tissue pathological image classification method and system based on artificial intelligence. The present application selects a histopathological region from a suspected pathological region, divides a sequence obtained by sequentially arranging the histopathological region into different subsequences, acquires pathological feature templates of the subsequences under each disease category, determines the disease category to which the histopathological region in the subsequence belongs according to the similarity between the texture distribution, blood vessel distribution characteristics of the histopathological region in the subsequence and the pathological feature templates, and acquires the feature templates of each subsequence under each disease category according to the pathological features of the histopathological region belonging to the same disease category and the pathological feature templates of the previous subsequence of each subsequence under each disease category. The present application realizes gradual and accurate matching of the pathological feature templates through dynamic and iterative optimization of the feature templates with the subsequences, and increases the accuracy of pathological feature matching of the intestinal mucosa pathological position.

The invention relates to an eye fundus image hierarchical disease category labeling method and device and computer equipment. The method comprises the steps of obtaining image features of a high-dimensional space; the method comprises the following steps: acquiring image features, acquiring levels for pre-labeling disease category labels on the image features, setting the number of multi-level classification heads according to the levels of the disease category labels, sequentially identifying target features in the image features according to each level of classification head, and setting the disease category labels of the corresponding levels for the identified target features. According to the invention, through category embedding and multi-head attention, the expression and discrimination capability of complex hierarchical labels can be significantly improved, accurate feature recognition can be carried out on the fundus image, and the accuracy of setting disease category labels is improved.

The application provides a medical text classification method and device based on prompt learning. The method comprises the following steps: obtaining prompt information for primary classification from original medical text based on event prior information and knowledge prior information, wherein the primary classification comprises a department category; filtering the original medical text, and inputting the filtered text and the prompt information into a large language generation model after integration; calculating the similarity between the result sequence output by the large language generation model and each secondary classification label representing a disease category under the primary classification, and taking the label category corresponding to the maximum value of the similarity as the secondary category output by the large language generation model. The application can realize primary classification based on the department category, and can also realize secondary classification based on the disease category under the primary classification, which is more consistent with the general cognition in the medical field, so that the classification result is more standardized; meanwhile, the classification label can be unfixed, so that multi-level text classification in an open domain can be effectively realized.

The invention discloses a tobacco leaf disease identification and control method, device and equipment and a storage medium, and relates to the technical field of plant disease identification, and the tobacco leaf disease identification and control method comprises the steps: constructing and training a disease identification model based on physical perception constraint and a hierarchical graph neural network; obtaining a to-be-identified tobacco leaf image, processing the to-be-identified tobacco leaf image through the disease identification model, and generating a disease category prediction result; and generating a disease control scheme according to a preset multi-modal large model and the disease category prediction result. The accuracy of tobacco leaf disease identification can be improved. Therefore, the reliability of a disease control scheme generated by a large model is effectively improved, the tobacco yield is improved, and economic loss is reduced.

The invention relates to the technical field of image recognition, in particular to a silkworm disease recognition system which comprises an image gray analysis module, a self-adaptive image enhancement module, a multi-dimensional feature extraction module, a feature saliency evaluation module, a dynamic weight distribution module and a disease classification mapping module. Identifying an overall gray level distribution condition and a local gray level fluctuation condition in the original image data of the target silkworm body to determine a dynamic adjustment parameter, and correcting the original image data to obtain enhanced image data; integrating multi-dimensional feature information in the enhanced image data into silkworm body state feature information; judging the significance degree of different types of information in the silkworm body state characteristic information for distinguishing different diseases so as to allocate the emphasis proportion of the characteristic information in the silkworm body state characteristic information; fusing the emphasis proportion and the silkworm body state feature information, and mapping the fused silkworm body state feature information to a preset silkworm disease feature template library to obtain disease categories; according to the invention, the accuracy of silkworm disease recognition can be improved.

The invention provides a novel field crop leaf disease recognition system based on style and multi-scale feature extraction, and relates to the technical field of crop leaf disease recognition. Comprises: an image acquisition module for acquiring an original image of a leaf in a field; the data preprocessing module completes size normalization, illumination enhancement and noise reduction; the style re-calibration module SRMB is used for extracting and weighting calibration features; the multi-scale feature fusion module EMSF is used for aggregating information of different scales; and the classification output module is used for carrying out global pooling, full connection and Softmax on the enhanced feature map and outputting a disease category. According to the invention, the problem of insufficient field crop leaf disease identification accuracy in the prior art is solved.

The application discloses a railway track disease detection method based on coordinate attention, comprising the following steps: acquiring a railway track image, performing adaptive pyramid scaling and normalization processing, and expanding a data set based on a Mosaic data enhancement method; adopting a lightweight backbone network based on FasterNet to extract multi-scale features of the railway track image; strengthening disease area related channel features based on an MSCAM multi-scale convolution attention module; connecting a coordinate attention CoordAtt module after the output of the MSCAM multi-scale convolution attention module to form a fused feature enhancement module, and obtaining disease area related channel features after association; fusing a perception loss on the basis of an original loss function of YOLOv8 to form a joint loss function, optimizing disease texture alignment in a deep disease area feature space, and forming enhanced features; inputting the features into a detection head to output disease categories, a bounding box and a confidence, and completing railway track disease detection. The application also discloses a system, an electronic device and a computer readable storage medium.