536 results about "Aided diagnosis" patented technology

The invention discloses a liver and gall lesion early screening system and method based on image fusion, and relates to the technical field of medical image processing and computer-aided diagnosis, and the method comprises the following steps: reconstructing a multi-modal image space-time coordinate system under a unified event time baseline, generating a respiratory displacement field and a magnetic sensitive pulse fingerprint, and constructing an artifact suspicion map; and performing anti-fact playback based on the artifact suspicion chart, performing frame-by-frame playback on the image acquisition sequence, quantifying artifact superposition tracks with consistent directions, and solidifying an artifact anchor point set. According to the method, space-time coordinates are constructed based on a unified event time baseline, a breathing displacement field and magnetic sensing pulse fingerprints are introduced, anti-fact playback, distortion kernel inference and residual decoupling are combined, artifact recognition and fusion intervention are achieved, and artifact closed-loop elimination is completed by judging threshold-driven fusion regulation and time reversal phase gating, so that the artifact recognition accuracy is improved. And the fused image authenticity is improved.

The invention relates to the technical field of medical image processing and computer vision, in particular to a medical image segmentation method and system based on deep learning, the method is based on a U-shaped encoder-decoder architecture, a DSAB module is introduced into an encoder, and context perception of a directional anatomical structure is enhanced through complementary directional space shift and CSA mechanism weighting; an MGCF module is designed in a decoder, and a parallel multi-scale convolution path and an AGCA mechanism are combined, so that multi-level features are efficiently fused to recover boundary details. Meanwhile, links of data preprocessing, Transform structure details, segmentation result post-processing and the like are supplemented, the model performance is improved through a mixed loss function and an optimization training strategy, and the method has remarkable advantages in segmentation precision and boundary definition and provides powerful support for clinical auxiliary diagnosis.

The invention relates to the technical field of medical image intelligent analysis and artificial intelligence auxiliary diagnosis, in particular to a medical image disease course prediction system based on an industrial neural network, and the system comprises a reference generation module which is used for obtaining static image data; processing the static image data by using a physical perception neural network to generate a pure ideal state reference; a perturbation simulation module; the industrial kinetic parameters are used as perturbation terms to be superposed to a pure ideal state reference, and a theoretical damaged state is generated; the projection verification module is used for acquiring real multi-modal observation data; generating a real residual error; generating a theoretical residual error based on the theoretical damaged state and the pure ideal state reference; calculating a manifold coupling confidence coefficient; the closed-loop correction module is used for performing inversion optimization on the industrial kinetic parameters; outputting a disease course prediction result according to the manifold coupling confidence coefficient; according to the method, the problem that a traditional medical model lacks physical consistency explanation is solved, and the credibility of artificial intelligence auxiliary diagnosis is remarkably improved.

The invention relates to the technical field of state monitoring, in particular to a wind turbine generator main transmission chain monitoring system based on cloud edge collaboration, which comprises a signal synchronization module, a fault cause identification module, a local classification module, a thermal distribution positioning module and a load evaluation module. According to the method, the key parameters of the main transmission chain are extracted in real time and the timestamps are structured, so that accurate signal synchronization and abnormity elimination are realized, the integrity and time sequence consistency of multi-source data are improved, and the fault indexes are extracted in combination with parameter fluctuation characteristics and threshold screening; a trigger list is constructed based on double judgment of peak value and rate, edge and cloud tasks are linked, node-level classification response is realized, a thermodynamic concentration area is positioned through parameter comparison of abnormal nodes and adjacent nodes, the accuracy and visualization effect of fault aggregation identification are enhanced, and the rotation speed and current change in the thermodynamic area are extracted to evaluate the load trend. And the dynamic identification of the local operation state change and the improvement of the auxiliary diagnosis capability are realized.

The invention discloses a CT (Computed Tomography) image pulmonary embolism segmentation and classification method combined with quality evaluation, which relates to the technical field of image processing, and comprises the following steps: inputting a 256 * 256 pulmonary embolism CT image and a quality score thereof into a quality score guide encoder, expanding a quality score dimension through linear transformation, and carrying out point product fusion with a feature map extracted by ResNet34 layer by layer to obtain a final product; generating multi-scale coding features; performing wavelet domain decomposition and reconstruction on the coding features through a wavelet transform fusion jump link module, and optimizing feature transmission; a multi-scale cross enhanced decoder is adopted to carry out multi-scale deconvolution fusion on the features, a segmentation result is output in combination with an efficient channel attention mechanism, meanwhile, pulmonary embolism existence judgment is output through a classification head, and the method provides powerful support for early diagnosis of pulmonary embolism, development of an image auxiliary diagnosis system and clinical application, and has good application prospects. Wide application prospects and profound social significance are realized.

The invention discloses a thyroid ultrasound image diagnosis method based on deep learning, and the method comprises the following steps: collecting a thyroid ultrasound original image set, and carrying out the preprocessing; performing focus segmentation on the standardized thyroid ultrasound image set; performing morphological constraint and boundary refinement; calculating the blood flow direction, blood flow velocity and blood flow power of each thyroid focus area and neighborhood; generating a preliminary fusion feature map based on a feature adaptive deep learning network, and fusing the preliminary fusion feature map with the thyroid focus blood flow feature vector set; obtaining a thyroid focus detection list through thyroid focus benign and malignant discrimination branches; and generating thyroid focus structured diagnosis data records based on the thyroid focus detection list, and writing the thyroid focus structured diagnosis data records into a computer-aided diagnosis system. According to the method, deep learning and multi-modal blood flow features are fused, intelligent diagnosis of the thyroid focus is achieved, and the method has the advantages of being high in precision, high in interference resistance and structured in result.

The invention discloses a hydroelectric equipment anomaly detection method based on physical mechanism guidance and sequential topological entropy fluctuation characteristics, and belongs to the technical field of hydroelectric equipment monitoring and fault diagnosis. The method comprises the steps that multi-source sensor data of hydroelectric equipment is collected and preprocessed; constructing a physical weighted distance function in combination with an equipment physical mechanism, and embedding time sequence data into a high-dimensional point cloud space; extracting persistent homology features through a sliding window, generating a persistent graph sequence and calculating topological feature indexes; a persistence graph entropy fluctuation index is provided, and anomaly detection is realized by quantifying time sequence fluctuation of topological entropy; and finally, visual output and an alarm mechanism are combined to assist diagnosis. According to the method, equipment physical characteristics and topological data analysis are fused, the problems that a traditional method is insufficient in nonlinear system modeling, insensitive to dynamic evolution and the like are solved, the early warning capacity and detection precision of early faults are improved, and the method is suitable for anomaly detection application of core equipment such as a water turbine and a generator.

The invention relates to the field of rail transit vehicle intelligent diagnosis, in particular to a train fault intelligent auxiliary diagnosis method fusing multi-source knowledge, which comprises the following steps of: constructing a multi-source knowledge document library, and performing text cleaning, segmented disassembly and vectorization processing to obtain a vector database; then obtaining a fault alarm list and capturing event information, and performing diagnosis according to the vector database to obtain a diagnosis suggestion; then time sequence operation parameters before and after the fault are extracted and analyzed, and a trend chart and characteristic indexes are generated to obtain an analysis result; constructing a multi-dimensional auxiliary analysis mechanism based on the diagnosis suggestion and the analysis result, and generating a maintenance suggestion in combination with historical maintenance work order information; and finally, generating a comprehensive auxiliary diagnosis report according to the maintenance suggestion, automatically generating a maintenance dispatching task list, determining a task execution sequence according to a preset priority scheduling algorithm, and executing the maintenance dispatching task list. The accuracy and timeliness of train fault diagnosis can be improved, and the safety, the operation and maintenance efficiency and the intelligent level of rail transit equipment are improved.

The embodiment of the invention provides an image processing and computer-aided diagnosis method, electronic equipment, a storage medium and a program product, and the image processing method comprises the steps: obtaining a to-be-processed medical image which comprises the information of a plurality of target parts; performing first feature extraction on the medical image to obtain medical image features of the medical image; performing second feature extraction on the medical image features to obtain part identification information of each target part corresponding to the plurality of target parts in the medical image features; taking the part identification information of each target part as guidance, performing part feature extraction based on the medical image features, and obtaining part features of each target part; and predicting text features of the text based on the part features of the target parts and the detection results corresponding to the target parts to obtain prediction detection results of the multiple target parts. Therefore, the target part in the medical image can be accurately positioned and detected without depending on an external mask.

The invention relates to the technical field of intelligent medical auxiliary diagnosis and neural function automatic evaluation, in particular to an NIHSS score automatic evaluation system for a stroke patient. Comprising a multi-mode induction and perception unit which is used as a front-end data entry and is used for collecting patient response in real time to generate a video stream containing depth and color information and a synchronous audio stream; the dynamic reference calibration unit is used for extracting kinematic characteristics to construct an individualized nerve reference template; the neural motion spectrum decomposition unit is used for generating a spectrum pathological feature vector for distinguishing myasthenia and ataxia; the opposite-side image rejection analysis unit is used for generating compensation and driving confidence for representing a real nerve driving intention; the cross-modal reflection analysis unit is used for generating a sensory pathway integrity index according to the nerve conduction velocity difference; and the collaborative scoring decision engine is used for mapping the multi-modal features into standardized NIHSS scores. According to the method, the interference of age and basic physique on scoring is effectively eliminated, and a high-precision comparison reference can be provided for subsequent abnormal judgment of the affected side.

The invention provides a hierarchical labeling method and device for a lung cancer pathological image, equipment and a storage medium. Relates to the technical field of medical image processing. The method comprises the following steps: carrying out digital scanning and image preprocessing on a lung cancer pathological section; labeling according to a three-stage progressive sequence of a macroscopic tissue area, a microscopic characteristic structure and a cellular level characteristic, wherein each stage adopts color coding and diagnosis priority rules exclusively corresponding to lung cancer subtypes and pathological characteristics; carrying out post-processing and standardized output on the labeling result; and training an AI model by using the standardized annotation data and applying the AI model to auxiliary diagnosis. Through a unified color coding system, a hierarchical labeling framework and a standardized data processing flow, the problems that an existing labeling system is disordered and poor in reusability are solved.

The invention discloses a Crohn disease focus automatic segmentation and activity evaluation system based on deep learning, which belongs to the field of medical artificial intelligence and comprises a data preprocessing unit, a focus automatic segmentation unit, a radiomics feature extraction unit, a feature screening and dimension reduction unit and an activity classification unit. According to the method, an nnU-Net deep learning segmentation model is combined with image omics feature extraction, multi-stage feature screening and machine learning classification technologies, so that full-process automation from CTE image preprocessing, focus automatic segmentation, feature extraction and screening to activity classification is realized. The system can efficiently and accurately segment the focus of Crohn's disease, automatically assesses the disease activity based on the screened key radiomics characteristics, significantly improves the consistency, objectivity and efficiency of diagnosis, and is suitable for clinical auxiliary diagnosis and scientific research analysis.

The invention provides a context feature aggregation pulmonary nodule segmentation method combining edge perception and multi-scale semantic guidance, and the method comprises the steps: obtaining a to-be-segmented pulmonary nodule CT image, inputting the to-be-segmented pulmonary nodule CT image into a trained EGP-Net network model, and obtaining a segmentation result; wherein in the EGP-Net network model, multilayer features of an encoder are sent to a global pyramid guide module to reconstruct and selectively introduce deep global semantics back to a shallow layer, and low-layer features are merged into an edge guide network to extract and reinforce fine-grained boundary information; the attention feature fusion module performs weighted attention fusion on global semantics and local edge features, and the multi-scale context decoder fuses multi-scale information through lightweight reconstruction such as sub-pixel convolution to generate a fine segmentation map. Experimental results show that the EGP-Net network model is superior to an existing advanced method in segmentation precision and boundary consistency, and has good clinical auxiliary diagnosis and precision medical application potential.

The invention discloses an intelligent diagnosis method for analyzing line hidden dangers based on dynamic electrical fingerprint characteristics, and belongs to the technical field of power line state monitoring and fault prediction. According to the method, a dynamic electrical fingerprint is constructed through high-frequency acquisition of line voltage and current instantaneous value signals and extraction of multi-dimensional features such as a time domain, a frequency domain and a time-frequency domain; establishing an electrical fingerprint database and a health model of different load working conditions in a line health state; during on-line monitoring, through three-level analysis of deviation comparison, state classification and trend prediction, graded early warning of line hidden dangers is realized, and abnormal feature items are output for auxiliary diagnosis. According to the invention, subtle changes of electrical parameters can be captured, the problems of low sensitivity, weak anti-interference capability and incapability of early warning in the prior art are solved, early recognition and advanced prediction of latent hidden dangers are realized, and the safety and reliability of a power system are significantly improved.

The invention provides a medical question and answer method fusing cross-modal hybrid experts, and the method comprises the steps: obtaining a medical image and a question text, and carrying out the preprocessing of the medical image and the question text, and obtaining an initial visual embedding matrix and an initial text embedding matrix; inputting the initial visual embedding matrix and the initial text embedding matrix into a trained medical question and answer model to obtain an answer text for the medical image and the question text; the medical question and answer model comprises a visual encoder, a text encoder, a position encoding module, a feature fusion module, a gating router, a hybrid expert module and an answer generator. A cross-modal Transform is utilized to realize deep semantic alignment of image and text features, a plurality of expert networks are dynamically scheduled through a gating router, specialized processing is performed for different feature vectors, and the adaptability of the model to complex lesions and rare cases is enhanced; multi-angle reasoning is carried out, accurate and credible answers are generated, and an efficient and reliable solution is provided for auxiliary diagnosis, medical training and scientific research analysis.

The invention belongs to the technical field of medical image processing, and particularly relates to a linear Transform general focus recognition method based on multiple perception and context guidance, and the method comprises the steps: extracting the multi-scale features of a medical CT image through a backbone network, obtaining the edge gradient features in parallel, making up the missing of focus boundary information through an edge perception feature enhancement module, and carrying out the recognition of the focus. And then global feature modeling under linear complexity is realized through a polarity perception feature interaction module, a high-discrimination-force multi-scale feature map is generated by using a context-guided feature pyramid network, and finally the model is optimized by combining a Hungary algorithm and a joint loss function based on an end-to-end detection architecture of set prediction. The problems that the focus boundary is fuzzy, feature interaction and calculation efficiency are balanced, and the focus and background separation degree is weak are effectively solved, double improvement of calculation efficiency and detection precision on massive medical image data is achieved, and reliable support is provided for clinical precise auxiliary diagnosis.

A consultation method and apparatus, and an electronic device and a computer-readable storage medium. The method comprises: acquiring target disease description information which is input by a user; and feeding back to the user a target diagnosis result about the target disease description information, wherein the determination of the target diagnosis result is related to the target disease description information and assistance diagnosis information, and the assistance diagnosis information comprises target profile information of a target consultation object that is indicated by the target disease description information. By means of the method, a high-quality consultation service can be provided for users.

According to the glaucoma multi-mode auxiliary diagnosis device and the electronic equipment, firstly, first feature extraction and second feature extraction are carried out on an eye fundus image and an OCT image respectively, then semantic spaces of an eye fundus image mode and an OCT image mode are aligned by utilizing comparison loss, and the first feature and the second feature are fused by utilizing a cross attention mechanism, so that an eye fundus image is obtained. According to the method, the two modal data are subjected to fusion to obtain fusion features, then the fusion features are subjected to feature extraction to obtain third features, and glaucoma classification judgment is performed based on the third features, so that global modeling of the two modal data can be realized, effective features are extracted to perform glaucoma classification judgment, and the accuracy and timeliness of diagnosis are ensured.

The invention relates to the technical field of medical image processing, in particular to a gynecological endoscope image intelligent analysis and cervical lesion precise diagnosis system. Comprising an image enhancement module, a differential geometric feature extraction module, a topology preserving manifold learning module, a curvature-blood vessel correlation analysis module, a biopsy point accurate positioning module, a real-time intelligent auxiliary diagnosis module, a clinical feedback module and a self-adaptive optimization module. Micro morphological change features are extracted through multi-scale curvature analysis; a topology sensitive encoder and a manifold alignment decoder are adopted, and a key topology structure is kept; establishing a correlation model of the tissue surface curvature change and the blood vessel morphology, and analyzing differential morphological characteristics; determining an optimal biopsy position based on the comprehensive characteristics; according to the method, the diagnosis result and basis are generated, visual display is provided, the detection rate of cervical lesions, especially early minimal lesions, is remarkably increased, the missed diagnosis rate is reduced, and multi-point biopsy is reduced.

The invention provides a pathology image report generation method combining hierarchical visual fusion and prototype alignment, and the method comprises the steps: collecting a pathology image and a diagnosis report as a data set, and carrying out the preprocessing of the pathology image in the data set; hierarchical visual context information fusion is carried out by constructing a hierarchical relation matrix; converting a lexical element sequence in a diagnosis report into a decoder target sequence and text embedding, inputting the text embedding and the fused visual features into a prototype mediated cross-modal semantic alignment module, taking the fused visual features and the text embedding as queries, and respectively taking a preset learnable prototype as a key and a value at the same time; training and cross-modal semantic alignment are carried out through a cross attention mechanism, and a pathological image report is generated. According to the method, the multi-scale modeling and semantic understanding capabilities of the model are remarkably improved through a dual mechanism of hierarchical visual fusion and prototype alignment, and a reliable path is provided for automatic pathological auxiliary diagnosis.

The invention provides a traditional Chinese medicine tongue diagnosis auxiliary diagnosis system based on artificial intelligence. The traditional Chinese medicine tongue diagnosis auxiliary diagnosis system comprises a tongue picture acquisition and preprocessing module, a tongue picture feature intelligent extraction module, a traditional Chinese medicine syndrome differentiation intelligent reasoning module and a diagnosis result integration and output module which are connected in sequence. Through multi-module cooperation and algorithm fusion, the pain points of traditional tongue diagnosis and an existing intelligent system are effectively solved.

The invention discloses a multi-mode brain dysfunction auxiliary diagnosis method based on a dynamic function connection network. A two-stage collaborative learning framework from an individual brain graph to a group relation graph is constructed. Firstly, an individual multi-modal fusion brain map is constructed, node features of the individual multi-modal fusion brain map are obtained through node regularization regression analysis of an rs-fMRI time sequence, an adjacent matrix is obtained through calculation of the brain interval grey matter volume difference of a T1 image, and individual enhancement characterization is obtained through map convolutional network fusion. And then constructing a group relationship enhancement graph, taking individual representation as node features, constructing a dual-channel adjacency relationship for distinguishing homologous / heterologous connection according to age and gender, obtaining final discriminative representation through dual-channel graph attention network aggregation, and performing classification diagnosis according to the final discriminative representation. According to the method, deep fusion of multi-modal information and explicit modeling of key biological variables are realized, and an effective tool is provided for accurate and explainable auxiliary diagnosis of brain diseases.

The invention relates to the technical field of medical images, in particular to a medical image intelligent detection and auxiliary diagnosis system based on deep learning, and the system comprises a data access module which is used for obtaining medical image data and clinical text data of a patient; the data fusion module is used for generating a focus feature vector and a text feature vector, and performing cross-modal alignment and fusion to generate a fusion feature vector; the diagnosis analysis module is used for executing focus detection, focus segmentation and focus classification tasks, generating a diagnosis result and generating a diagnosis label based on the diagnosis result; the decision generation module is used for mapping the generated execution result to a preset medical knowledge base and performing deep reasoning to generate an auxiliary diagnosis decision; the decision auditing module is used for performing confidence scoring on the auxiliary diagnosis decisions and selecting the auxiliary diagnosis decision with the highest confidence score as the final auxiliary decision; and the data visualization module is used for carrying out visualization processing on the auxiliary decision and the diagnosis result.

The invention discloses an intraocular light field simulation method and system based on physiological constraint and pathology traceability, the system takes a Physics Informed Kolmogorov-Arnold Net (PI-KAN) network as a core, namely physical information KAN, the method comprises the following steps: obtaining personalized parameters of eyeballs; 5-dimensional light field parameters including space, wavelength and time are input into a pre-trained PI-KAN model for light field solving, a three-layer network architecture including an input layer, a hidden layer and an output layer is established, a physical information edge function is constructed, and training is performed through fusion of a Helmholtz equation and a loss function of boundary conditions; generating an OCT image based on the light field solved by simulation; by analyzing side function mapping, visualization and pathological traceability of a light field propagation physical mechanism are realized. According to the method, the sparsity and interpretability of PI-KAN are utilized, the problems that a traditional method is low in calculation efficiency, difficult in high-dimensional modeling, weak in physical constraint and poor in interpretability are solved, millisecond-level, high-precision and interpretable simulation of the eye light field is achieved, and the method is suitable for ophthalmic clinical auxiliary diagnosis, surgical planning and equipment optimization.

The invention discloses an intelligent auxiliary diagnosis and grading system for gastrointestinal tumor endoscopic images, which belongs to the technical field of medical image processing and computer-aided diagnosis and comprises a multi-modal image preprocessing and segmentation module, a lesion feature extraction and diagnosis module, a grading and depth evaluation module and a closed-loop feedback optimization module. The system receives white light, a narrow band and an amplified endoscopic image, adaptive segmentation is performed to obtain a lesion area, mucous membrane morphology, capillary and gland features are extracted, Paris classification, Vienna classification and infiltration depth evaluation are realized, and segmentation parameters are subjected to closed-loop optimization according to classification confidence. And intelligent auxiliary support is provided for early diagnosis and treatment decision of gastrointestinal tumors.

The invention discloses a cognitive function digital evaluation system and method based on dynamic visual tracking, four attention function tests are integrated in one system for the first time, the attention function of an individual can be comprehensively evaluated, and a more comprehensive and more systematic cognitive function evaluation report is provided; compared with a traditional single-function test, the cognitive condition of an individual can be known more comprehensively, and a more valuable reference is provided for the fields of educational evaluation, clinical diagnosis, human resources and the like; for example, in education evaluation, a teacher can comprehensively understand attention characteristics of students through the system and make a more targeted teaching plan; in clinical diagnosis, a doctor can comprehensively evaluate the cognitive function of a patient to assist diagnosis and treatment.

The invention relates to the technical field of medical image auxiliary diagnosis, and provides an intervertebral joint osteoarthritis image feature evaluation method based on deep learning, which can synchronously identify five types of FJOA image features such as joint space stenosis, osteophyte, hypertrophy, subchondral bone erosion and subchondral cyst, and improves the comprehensiveness and efficiency of evaluation. The nnU-Net model is adopted to perform high-precision segmentation on an intervertebral joint region, so that the positioning precision is effectively improved; by introducing a shared feature extraction network based on ResNet-18 and five parallel classification sub-networks, joint modeling of multi-scale semantic information is realized, and the recognition accuracy and generalization ability of the model are improved; according to the method, a Grad-CAM technology is combined to generate an activation thermodynamic diagram, a model interpretation basis is superposed to an original image, visual display of an evaluation result is realized, interpretability and clinical applicability of the model are enhanced, and FJOA image features in axial lumbar vertebra CT images from two central data sets can be quantitatively evaluated so as to comprehensively quantify individual FJOA image features.

The invention discloses a hematemesis volume intelligent determination method based on image identification and data analysis, and relates to the technical field of artificial intelligence auxiliary diagnosis, and the method comprises the following steps: constructing a spattering phase and spectrum combined observation baseline through a blood spattering imaging sequence, analyzing a light spot transient track through a time-frequency phase-locked calibration mechanism, extracting high-frequency reflection characteristics, and determining the hematemesis volume according to the high-frequency reflection characteristics. Generating a light spot prior set for interference identification; according to the distribution characteristics of the light spot prior set, multi-angle linear polarization image sampling and multi-interval adaptive exposure adjustment are executed, a stable coupling characteristic stack of color characteristics and texture characteristics is constructed, and a unified spectrum reference is provided for track identification processing. By introducing phase-spectrum combined observation, multi-angle polarization sampling and a dynamic regulation and control mechanism, a steady-state feature stack and a track topological graph are constructed, light spot interference stripping and volume fitting stabilization are achieved, the accuracy, real-time performance and robustness of hematemesis volume recognition are improved, and the method has remarkable clinical application value.

The invention discloses a new mutation-oriented pathogenicity prediction method, which comprises the following steps of: acquiring new mutation site data to be analyzed, and generating a numerical feature vector containing evolution conservative property, function prediction score and quantitative clinical evidence; constructing a feature extractor through the numerical feature vector; inputting a training data set containing a label sample and a label-free sample into the feature extractor, and updating the network parameters of the feature extractor and each header; and predicting target variation by using the feature extractor after parameter updating and the supervised classification head to obtain a pathogenicity probability, and calculating a contribution value of each feature in the numerical feature vector to the pathogenicity probability. According to the technical scheme provided by the invention, the auxiliary decision-making information conforming to the diagnosis habit of a doctor is output, the workload of manually interpreting the variation with unclear meaning is greatly reduced, and the end-to-end intelligent processing from the original sequencing data to the clinical auxiliary diagnosis is realized.

The invention relates to the technical field of artificial intelligence, in particular to a special child disease classification method based on particle-ball cross-granularity knowledge collaborative feature selection. The method comprises the following steps: firstly, providing a cross-granularity knowledge collaboration method based on granules and balls; further designing a fuzzy rough set model based on particle-ball cross-granularity knowledge collaboration to perform feature selection; according to the method, feature knowledge under different granularity levels is mined and coordinated to realize more robust and more efficient feature selection, so that the accuracy, generalization ability and interpretability of a classification model are improved, and a more reliable technical tool is provided for early screening and auxiliary diagnosis of diseases of special children.