138 results about "Disease category" patented technology

The invention relates to the technical field of crop disease recognition, in particular to a crop early disease recognition method and system based on time sequence feature fusion, and the method comprises the steps: obtaining an RGB image and a temperature feature image of a target leaf, and collecting multispectral feature data through a spectral imaging device; based on the time sequence image sequence, generating a coordinate transformation matrix of the first frame image by using a PnP algorithm, and aligning subsequent images according to the coordinate transformation matrix; multi-modal features are extracted by using a deep neural network, and the deep neural network comprises a 3D convolutional layer and an adaptive attention mechanism and is used for extracting features of space and time dimensions; realizing cross-modal feature fusion through a graph convolutional network based on the spectral data and the multi-modal features; inputting the fused features into a classifier, and generating the disease category and the disease severity of the target leaf based on the output of the classifier; outputting the disease transmission risk and the early warning result, and capturing the tiny dynamic change in the disease development process.

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 discloses a disease segmentation method and system based on a language large model. The method comprises the steps of obtaining a to-be-segmented disease image; an image encoder based on a fusion Transform module and an Adapter module encodes the disease image, and the encoded image is output and embedded; generating a target segmentation content prompt based on a large language model semantic prompt encoder; and performing mask decoding by using a mask decoder according to the encoded image block embedding and the target segmentation content prompt to obtain a disease image after semantic segmentation. According to the invention, comprehensive, complete and adaptive segmentation is carried out on disease types by using a powerful visual large model migration network, and the accuracy of automatically identifying new disease types under the condition of less sample training is improved.

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 discloses a road disease detection method based on improved YOLO-World in the technical field of computer vision, and the method comprises the following steps: 1) carrying out lightweight design, and integrating a FasterNet module in a backbone network; step 2) model architecture improvement: an SPPCSPC module is integrated in a backbone network, and the small target detection capability is enhanced through multi-scale pooling and feature fusion; 3) attention mechanism embedding: embedding a CBAM module in the detection head, and cooperatively improving the feature focusing capability through a channel and space attention; step 4) model optimization and training: based on the self-built data set, adopting an AdamW optimizer and mixing precision training, and iterating to model convergence; and 5) disease detection output: carrying out preprocessing, multi-scale feature extraction, attention weighting and bounding box prediction on the input image, and outputting disease category and position information, thereby improving the precision and speed of road disease detection, and enhancing the adaptability of the model to complex scenes (such as low illumination, shielding and the like).

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 discloses a disease full-period management system based on a deep neural network, and the system comprises a collection time sequence planning module, a disease biomarker data collection module, and a compliance standard assessment module. The method comprises the following steps: acquiring previous disease diagnosis elements such as specific disease category proportion and attack frequency of each patient in each time window of a historical period from a target hospital patient platform, analyzing a disease period adaptive value according to the previous disease diagnosis elements, and further determining an acquisition time sequence plan when the patient is discharged from a hospital. And the compliance standard evaluation module is used for acquiring a compliance evaluation value of the discharge collection time sequence plan of each patient in the current period, judging whether the compliance reaches the standard or not, sending out an early warning prompt to the substandard patient, and recording the substandard patient as a to-be-analyzed patient. And the disease outcome state analysis module is used for analyzing the disease outcome states of the to-be-analyzed patients at each acquisition time point, carrying out early warning prompt according to the analysis result, and realizing accurate management and monitoring of the whole period of the diseases of the patients through multi-module cooperation.

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 invention relates to the technical field of data processing, in particular to a chronic disease monitoring method, system and device and a storage medium, comprising: acquiring disease information of a patient, and determining the disease category of the patient according to the disease information; obtaining a to-be-collected information type associated with the illness state type according to the illness state type; acquiring follow-up visit body parameters of the patient according to the type of the to-be-collected information and a preset return visit period; establishing follow-up visit body change trend information according to the follow-up visit body parameters; performing trend prediction on the follow-up visit body change trend information based on a preset prediction period to obtain body prediction change trend information of the patient in the prediction period; generating a monitoring analysis result according to the body prediction change trend information and the illness state information; by setting the fixed return visit period, collecting the specific return visit indexes of the patient and combining the indexes and the illness state, the illness state development trend of the patient is predicted, and therefore the monitoring efficiency of the chronic disease patient in the treatment process is improved.

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 provides a small sample tympanic membrane image recognition and diagnosis system based on artificial intelligence, and the system comprises a category matching module, a category enhancement module, a knowledge enhancement module and a diagnosis generation module, calculating the similarity between the input image and at least one image category, and determining the matching category of the input image according to the similarity; the category enhancement module is used for determining a disease category corresponding to the input image according to the input image, the matching category and the inquiry; the knowledge enhancement module is used for determining a diagnosis and treatment scheme of the disease category according to the structured knowledge corresponding to the matching category of the input image; the diagnosis generation module is used for generating a diagnosis report corresponding to the input image.

The invention relates to a track structure disease recognition method and device based on a fiber bragg grating array, and belongs to the technical field of track disease recognizing.The method comprises the steps that effective information extraction is conducted on an initial vibration signal, an effective vibration signal is obtained, and therefore data of the fiber bragg grating array can be screened; the data volume of the to-be-processed data is reduced; and sequence calculation is carried out on the effective vibration signals based on a standard basic database to obtain a plurality of OSA sequences, so that error estimation of each sensing point is obtained, frequency characteristics of the signals can be reserved on the premise of reducing noise influence, and therefore, when region division is carried out on the plurality of sensing points according to disease data of error estimation, the accuracy of region division is improved. According to the method, the accuracy of area division is improved, curve fitting and disease category judgment can be carried out according to all error estimation in the sensing detection area, a disease recognition result is obtained, disease recognition of the track structure is achieved, and the accuracy of disease recognition 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.

Described herein are systems for disease detection from a biological sample using a machine learning-based (ML-based) disease-detection model trained on a dataset of detection events. Also described are methods for detecting a disease category from a biological sample received from a subject, and further detecting the specific disease type within the disease category using the systems and ML-based disease detection models. Also described are methods for monitoring progression of a disease in a subject by analyzing a biological sample using the systems and ML-based disease-detection model disclosed herein.

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 system uses machine learning to predict diseases or conditions and assess disease risk progression from routine healthcare test results, either at a single time point or across multiple time points. The system predicts the presence or absence of a disease or condition, including screening. Routine healthcare test results include a number of typical clinical measures (such as forty, fifty, sixty measures) each with a separate value. The system makes predictions regarding diseases or conditions. The system is configured to estimate disease probability, stratify patients by risk level, and generate risk trajectories over time. The framework is designed to be extensible to future disease categories, novel biomarkers, and evolving machine learning models. The methodology applies to any disease or condition where blood-based, genetic, imaging, environmental, or real-time physiological data provide diagnostic insights.

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 provides a data processing method, medical equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a target gestational week stage of a target fetus, and determining a plurality of disease categories corresponding to the target gestational week stage in a disease category database based on the target gestational week stage; acquiring disease characterization information of the target fetus, wherein the disease characterization information comprises one or more of a target section image and a disease description text of the target fetus; and for each disease category corresponding to the target gestational week stage, based on the disease characterization information of the target fetus, calculating the disease probability that the target fetus suffers from the disease of the disease category. And based on each disease probability, determining a final disease category in a plurality of disease categories corresponding to the target gestational week stage, and outputting a standard disease name corresponding to the final disease category. According to the invention, the realization difficulty of subsequent medical data statistics can be reduced.

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.