255 results about "Disease classification" patented technology

The invention discloses a multi-modal medical image data intelligent processing system, relates to the field of medical image analysis, and is applied to multi-modal medical image whole-process analysis of CT, MRI, PET, ultrasound and the like. According to the system, different modal image features are extracted and fused through a cross-modal manifold fusion network; a semantic guidance dynamic registration engine optimizes registration parameters to ensure that the registration error is less than or equal to 1.5 mm; the multi-task collaborative diagnosis network realizes multiple tasks such as disease classification; the clinical knowledge embedding and interpretable module generates a structured report and is in butt joint with an HIS system. Meanwhile, the model is optimized through a federated learning architecture, the adaptability of newly added data is improved by more than or equal to 20%, and intelligent processing and analysis of multi-modal medical images are realized.

The invention provides a knowledge and data fused medical content image-text generation system and method. Relates to the technical field of digital medical treatment and artificial intelligence. The multi-modal feature extraction module is used for extracting multi-modal features from the multi-modal data; the multi-modal feature fusion module is used for carrying out cross-modal dynamic fusion on the multi-modal features to generate cross-modal high-consistency fusion features; the model training optimization module is used for configuring a soft and hard target loss function and a parameter optimizer; the information analysis and reasoning module is used for carrying out semantic matching on the fusion features, the disease classification and the clinical path based on a dynamic medical knowledge network, and generating disease prediction probability distribution and a semantic reasoning path; and the image-text report generation module is used for generating an image-text report containing a diagnosis conclusion and an interpretation basis according to the prediction probability and the semantic reasoning path. According to the invention, intelligent and precise generation of medical contents can be realized.

The invention relates to the field of image analysis, in particular to a temporal bone disease classification method and system based on a multi-modal medical image fusion technology. The method comprises the following steps: acquiring a multi-modal image of a patient, performing adaptive distortion correction, and generating a standardized image set; performing layer-by-layer anatomical structure semantic segmentation and multi-modal image fusion on the standardized image set to construct an image fusion framework; according to the image fusion framework, performing intelligent recognition on the fine structure of the temporal bone, and constructing a personalized temporal bone anatomical structure chart; performing tissue function state analysis and digital pathology dynamic simulation based on the personalized temporal bone anatomical structure chart, and constructing a digital pathology model; and performing intelligent pathological feature classification based on the digital pathological model to obtain an intelligent classification report. According to the method, rapid, efficient and accurate temporal bone disease classification is realized.

The invention discloses a hollow slab girder bridge maintenance decision-oriented hierarchical knowledge graph construction method, which comprises the following steps of: constructing a hollow slab girder bridge disease classification and maintenance decision general knowledge graph, combing disease types and evaluation indexes of a hollow slab girder bridge through cross-standard feature alignment and rule fusion, and defining each disease and the index thereof in a variable manner so as to construct a hollow slab girder bridge disease classification and maintenance decision-oriented hierarchical knowledge graph. The weight of each disease index is determined based on a fuzzy-Bayesian hybrid reasoning algorithm, and a complete general knowledge graph structure is formed; extracting disease information based on a detection report of a single bridge, endowing each disease with a maintenance suggestion by using a general knowledge graph, and forming a maintenance decision knowledge graph special for the bridge; and dynamically updating the special knowledge graph, and adjusting disease information and maintenance suggestions according to a subsequent detection report of the bridge to ensure timeliness and accuracy of the graph. According to the method, the problems that knowledge is dispersed and systematization is difficult in traditional hollow slab girder bridge maintenance decision making are solved, and scientificity and efficiency of bridge maintenance management are improved.

The invention relates to a steel bridge disease detection and identification method based on a large language model, and belongs to the technical field of artificial intelligence and civil engineering crossing. According to the method, a cross-modal feature alignment mechanism is constructed through a pre-trained multi-modal large language model by fusing a steel bridge image and a field customized text prompt, and a cascade detection process of'component identification-disease classification-region segmentation 'is realized. Comprising the following steps: designing a structured text prompt word bank to enhance semantic consistency, and dynamically fusing general knowledge and instance features in combination with a mixed prompt mechanism; a multi-level cross-modal alignment strategy is adopted to generate an anomaly graph, and a disease area is accurately positioned; a visual prompt enhancement module is introduced to improve the multi-scale feature discrimination ability, and the robustness in a complex environment is adjusted and optimized through data self-adaption. Under the condition of few samples or even zero samples, high-sensitivity detection and pixel-level segmentation of steel bridge cracks, corrosion and other diseases are achieved, and the problems that a traditional method is low in efficiency, poor in generalization, high in labor cost and the like are effectively solved.

The invention belongs to the related technical field of brain image processing, and provides an Alzheimer's disease classification method and system based on topology perception and a group hypergraph in order to solve the problem of inaccurate classification of the Alzheimer's disease in the prior art. Constructing a dynamic function connection network sequence through a sliding window strategy; a local topology perception encoder and a global topology perception encoder are respectively used for extracting local topology features and global topology features of each time window, deep interaction and fusion are carried out, and comprehensive feature representation of a tested level is generated; according to the method, each subject is used as a hypergraph node, hyperedges are constructed on the basis of comprehensive feature representation of a subject level and by combining feature similarity calculated by diffusion tensor imaging features and clinical embedded features of the subject, then a group hypergraph is constructed, a classification result is obtained by using a hypergraph neural network, and the early classification diagnosis accuracy of the Alzheimer's disease is effectively improved.

The invention discloses an artificial intelligence auxiliary medical decision-making system, and relates to the technical field of medical assistance. Through the detection suggestion module, preliminary reasoning and observation index judgment can be performed according to patient chief complaint in combination with basic information, and detection suggestions are given in combination with detection cost budget of a patient; through the diagnosis module, preliminary judgment can be made according to detection abnormity, disease classification is judged based on the current diagnosis result, further detection suggestions are given, and finally a doctor makes further diagnosis or treatment suggestions; through the treatment suggestion module, disease causes can be judged according to the diagnosis result, and detection cost budget of a patient is combined, so that the diagnosis and treatment efficiency is improved. Giving out treatment suggestions; the detection suggestion module, the diagnosis module and the treatment suggestion module jointly act to form a medical decision-making system, non-common diseases can be found easily, the problem of cross-department missed diagnosis is solved, the medical budget planning function is achieved, and doctors and patients can provide accurate treatment suggestions easily.

The invention belongs to the technical field of intelligent diagnosis, and particularly relates to an AI combined MRI and clinical JIA diagnosis system and a storage medium. According to the system disclosed by the invention, the early auxiliary diagnosis of the juvenile idiopathic arthritis is carried out by combining artificial intelligence with multi-dimensional and multi-modal information of multi-sequence MRI images of knee joints of children and various clinical information. The main technology of the method is child knee joint tissue segmentation based on deep learning, a multi-dimensional feature extraction strategy based on a segmentation result, and disease classification based on multi-modal feature integration and deep learning. By integrating the multi-dimensional features of the multi-sequence MRI images and fusing different modal features such as image information and clinical information, an auxiliary diagnosis result with high accuracy can be provided. The technology provided by the invention is beneficial to the realization of early diagnosis and early treatment of juvenile idiopathic arthritis, and has a very good application prospect.

The invention provides an intelligent mental disease recognition method and device based on multi-modal data and a medium. The method comprises the steps that illness state self-described text information and ear image data of a to-be-tested person and impedance and / or temperature information of all designated acupoints in the auricular concha area and the helix area of the to-be-tested person are obtained; performing local feature extraction on the ear image data to obtain an image feature vector; capturing measured value feature vectors between impedance and / or temperature information of different acupoints by using a multi-head attention mechanism; extracting semantic feature vectors of the illness state self-described text information; and forming a multi-modal fusion feature vector from the image feature vector, the measured value feature vector and the semantic feature vector, inputting the multi-modal fusion feature vector into an intelligent disease identification model for classification, and outputting a mental disease prediction result. According to the method, the ear image, the impedance and / or temperature information of each acupuncture point and the multi-modal data of the text information of the patient are fused, and the deep learning model is combined to realize disease classification, so that the recognition efficiency and accuracy are improved.

The invention discloses a semantic enhancement auxiliary inquiry method and system based on a medical knowledge graph. According to the method, natural language processing, knowledge graph construction and reasoning and other means are fused, and by means of hierarchical entanglement state tracking, dynamic prompt template design, undirected isomeric graph construction and other methods, patient information is accurately obtained through multiple rounds of dialogues, the semantic understanding and reasoning ability is enhanced, an optimization scheme is provided for intelligent inquiry, and the intelligent inquiry efficiency is improved. And disease classification and treatment schemes can be determined in an assisted manner.

The invention discloses a medical data processing method, system and device based on big data and a medium. The method comprises the steps that medical image data and medical text data of a patient are acquired; performing feature extraction on the medical image data to obtain an image feature vector, and analyzing the medical text data to obtain a text feature matrix; inputting the image feature vector and the text feature matrix into a multi-modal fusion model, calculating an association weight of the image feature and the text feature, and obtaining a fusion feature representation; and establishing a disease prediction model according to the fusion feature representation, performing disease classification diagnosis on the patient, and generating a structured diagnosis report according to a diagnosis result. According to the method, the medical image data and the text data are deeply fused through the multi-modal fusion model, the complementary advantages of different modal data are fully utilized, and the problem of information missing possibly existing in a single data source is avoided.

A plasma digital marker mining and disease classification device comprises a plasma sample spectrum acquisition module used for determining a digital marker sorting cluster in a plurality of plasma samples; the plasma candidate digital marker selection module is used for iterating plasma spectrum samples in the initial disease classification device based on the digital marker sorting cluster to obtain a plasma candidate digital marker cluster; the pathogenic protein digital marker cluster acquisition module is used for acquiring pathogenic protein digital marker clusters corresponding to spectral absorption peaks of various related pathogenic proteins; the plasma digital marker mining module is used for determining an intersection of the plasma candidate digital marker cluster and the pathogenic protein digital marker cluster as a plasma digital marker; and the disease classification device construction module is used for training a disease classification device according to the plasma digital markers to obtain a trained disease classification device. By adopting the technical scheme, a disease classification device capable of accurately distinguishing MCI patients can be constructed.

The invention relates to a bridge superstructure safety risk assessment method and system and a storage medium. The method comprises the following steps: acquiring disease score deduction information of a technical condition of a bridge superstructure; obtaining bridge superstructure technical condition scoring data of the sample bridge, bridge superstructure score-deducted indexes and disease classification categories of the score-deducted indexes, and analyzing common indexes in the score-deducted indexes according to the score-deducted indexes; calculating a deduction value of the disease classification category of the common index at each scale based on an LSHADE algorithm, and calculating a safety risk score of the bridge superstructure of the sample bridge; dividing safety risk levels; constructing a bridge safety risk evaluation model by adopting a CatBoost ensemble learning method, and training the bridge safety risk evaluation model by utilizing a frequency reduction coefficient and the like of a sample bridge; and evaluating the safety risk level of the bridge superstructure by using the trained bridge safety risk evaluation model. The method is high in evaluation efficiency, low in cost and high in accuracy.

The invention provides a diagnosis and treatment model training method and system and electronic equipment, and relates to the technical field of medical diagnosis and treatment. The method comprises the following steps: performing field pre-training on a basic large language model by utilizing a medical corpus to obtain a pre-trained diagnosis and treatment model; corresponding diagnosis reasoning process information is generated for each piece of target medical record data in the target medical record data set, a supervision fine tuning data pair set is obtained, and each supervision fine tuning data pair in the supervision fine tuning data pair set comprises medical record information and a diagnosis conclusion containing the diagnosis reasoning process information; and performing supervision fine tuning training on the pre-trained diagnosis and treatment model by using the supervision fine tuning data pair set to obtain a trained diagnosis and treatment model. According to the method, the diagnosis and treatment model can master professional contents such as disease classification, clinical symptoms, diagnosis processes and drug treatment, and the effect of simulating a doctor to gradually analyze the symptoms to obtain a diagnosis result is achieved, so that the accuracy of the diagnosis and treatment result is improved.

The invention relates to a citrus disease identification method based on a lightweight multi-scale feature fusion network of an attention mechanism. The method comprises the following steps: acquiring a citrus huanglongbing image sample for preprocessing; constructing a Citrus Huanglongbing image recognition model; inputting the training set into a Citrus Huanglongbing image recognition model for training; and inputting the preprocessed to-be-detected citrus huanglongbing image into the trained citrus huanglongbing image recognition model to obtain a citrus huanglongbing image recognition result. According to the method, the model can extract multi-scale and multi-granularity feature information by setting different expansion coefficients, so that rich image features are extracted, and the accuracy of model recognition is improved; according to the method, the model parameters are not increased while the receptive field of the feature map is expanded by using the expansion convolution, and the model uses the depth separable convolution to reduce the parameter quantity of the model, so that the model is very light, and the Candidatus Liberobacter asiaticum image recognition model shows the optimal performance in the disease classification task while being light.

The invention discloses an ultrasonic image evaluation processing method and device for meibomian gland dysfunction, and relates to the technical field of data processing. A meibomian gland dysfunction disease classification model is constructed based on a lightweight first-order index integral method decoder module and a second-order explicit Adams method decoder module; and the identification capability of the meibomian gland classification model on a high-noise region and a small target region in the ultrasonic image is improved, so that the meibomian gland in the ultrasonic image can be subjected to high-precision classification evaluation processing by the meibomian gland dysfunction disease classification model. Due to the adoption of the lightweight decoder module, the model parameters of the meibomian gland dysfunction classification model are reduced, and the scale is lighter, so that the occupation of computing resources during the operation of the meibomian gland dysfunction classification model is reduced. The encoder-decoder model is trained after the unlabeled data set is pre-trained based on the fine tuning data set, so that self-supervised training of the meibomian gland dysfunction disease classification model is realized, and the training cost and time are reduced.

The invention discloses an Alzheimer's disease classification method and system based on multi-modal evidence deep learning, and relates to the technical field of machine learning, and the method comprises the steps: obtaining an Alzheimer's disease multi-modal data set; inputting samples in the Alzheimer's disease multi-modal data set into a pre-established Alzheimer's disease classification model, and adjusting parameters of the Alzheimer's disease classification model to be optimal through an error back propagation algorithm to obtain an optimized Alzheimer's disease classification model; and obtaining subject sample data, inputting the subject sample data into the optimized Alzheimer's disease classification model, and outputting to obtain a classification result.

The invention discloses a multi-omics data integration and classification method, system and device based on hierarchical attention, and is applied to the field of precise medical big data analysis. The method comprises the following steps: firstly, generating feature embedding and feature importance scores through a plurality of parallel feature-level attention modules; then, embedding and inputting all the characteristics of the omics into a unified omics-level attention module, and generating omics embedding and omics importance scores; and finally, a classification prediction task is executed based on omics embedding, and a classification result is output for disease classification. The invention completely abandons a traditional dependency graph convolutional network and an integration normal form of variants of the dependency graph convolutional network, and provides a universal hierarchical attention integration architecture. The framework supports classification tasks of any complex diseases, is not limited by omics data types and combination modes, not only is remarkably superior to a traditional integration normal form in classification performance, but also shows a unique negative generalization distance, and proves that the framework has excellent generalization ability. Meanwhile, features and omics importance scores automatically output by the model provide a powerful analysis tool for biomarker discovery and precise diagnosis and treatment of complex diseases.

The invention particularly relates to a respiratory disease diagnosis device and method, and the device comprises a respiratory sound signal collection unit which is used for obtaining a current respiratory sound signal; the breathing sound signal preprocessing unit is used for performing segmentation processing and denoising processing on the current breathing sound signal to obtain a preprocessed breathing sound signal; the feature extraction unit is used for extracting multi-modal features of the preprocessed breath sound signals; and the detection unit is used for inputting the multi-modal features into a preset deep learning network model to obtain a respiratory system disease detection result. Therefore, through multi-modal feature extraction, dynamic fusion and an efficient classification strategy, the problems of relatively low feature identification degree, relatively low disease classification accuracy and the like in the process of researching disease diagnosis based on the breath sound signals in related technologies are solved, and the detection performance of chronic respiratory system diseases based on the breath sound signals is remarkably improved.

The invention discloses an Alzheimer's disease diagnosis classification method based on a multi-modal nerve image, which is used for auxiliary diagnosis of Alzheimer's disease. The method comprises the following steps: firstly, respectively extracting image features of sMRI and PET brain images by using a self-attention vision converter, extracting global information, improving the feature discrimination capability, and enabling a model to capture a remote dependency relationship more easily, and secondly, realizing complementary fusion of the two image features by using an interactive attention fusion network. And finally, using a Stacking ensemble learning framework as a classifier. According to the method provided by the invention, the pathological information of sMRI and PET brain images is combined, the Alzheimer's disease classification accuracy is improved, a doctor can be assisted in diagnosis, and the method has a wide application prospect in the field of computer-aided diagnosis.

The invention discloses a construction method of an Alzheimer's disease classification model based on multi-modal feature fusion, and relates to the field of medical image processing. The problems that in the prior art, an Alzheimer's disease diagnosis method based on deep learning is insufficient in multi-modal data utilization, high in model complexity, high in medical cost and the like are solved. According to the method, sMRI images and clinical scale data serve as input data, the 3D MLP-Mixer obtains image detail information and space information, the GRU is used for extracting behavior cognition information, the ACF module is used for fusing the behavior cognition information and the image information extracted by the 3D MLP-Mixer, and therefore the patient recognition capacity and the cross-dataset generalization performance of the model are improved. The method is also suitable for the field of Alzheimer's disease image processing.

The invention provides a cardiovascular and cerebrovascular disease classification model construction method, which comprises the following steps: receiving dynamic signal data of a cardiovascular and cerebrovascular disease patient, and constructing a dynamic signal matrix; global pathological features of patients with cardiovascular and cerebrovascular diseases are collected to serve as static feature vectors, and time dimensions of the static feature vectors and the dynamic signal feature matrix are unified to construct a fusion feature matrix; constructing a common disease association network; when patient group grouping is carried out, similarity mapping from individuals to groups is carried out through similarity calculation of features of each time slice and a group feature center to generate patient group features, and the patient group features are aligned with a patient feature center matrix; and constructing a deep classifier for cardiovascular and cerebrovascular disease classification, and finally outputting a classification result by the classifier. The method has significant breakthroughs in the aspects of dynamic feature modeling, disease relevance modeling and personalized adaptation capability, and an efficient and accurate technical means is provided for cardiovascular and cerebrovascular disease classification.

The invention relates to the technical field of digital medical treatment, and particularly discloses an artificial intelligence diagnosis system for nervous system diseases. The system comprises a data acquisition module, a preprocessing module, a multi-modal feature extraction module, a feature fusion module, a disease classification module, an interpretability analysis module and a doctor feedback and model updating module. The system supports acquisition and fusion of medical images, electroencephalogram signals, biochemical indexes, questionnaire information and wearable device data, constructs unified feature representation through a weighted attention mechanism, and outputs a prediction result of nervous system diseases based on a deep learning model. And meanwhile, the system is combined with a Shapley value method to realize the interpretability of the diagnosis process, and supports closed-loop return of doctor feedback information and model increment updating. The nervous system disease diagnosis accuracy and early recognition capability are improved, and the method has relatively high clinical deployability and adaptive optimization capability.

The invention discloses an Alzheimer's disease classification method and device based on a hybrid hypergraph neural network. The method comprises the following steps: firstly, preprocessing diffusion magnetic resonance imaging data of a brain; then, extracting three different scales of network topology features in the dMRI matrix, including a fiber strength matrix, a path redundancy matrix and a node efficiency matrix, and splicing the features into a comprehensive node feature; constructing a hypergraph structure based on first-order neighbors, and newly adding hyperedges of 12 nodes with the highest degree centrality to capture high-order topological information in a brain network; then, the extracted node features are processed by using a hybrid hypergraph convolution model, global structure information is extracted by combining a spectral domain convolution model and local structure information is extracted by combining a spatial domain convolution model, and the classification accuracy is improved; batchNorm normalization and Dropout regularization are used to prevent over-fitting and improve the generalization ability; and finally, classifying the processed data through a full connection layer. According to the method, a more accurate Alzheimer disease classification result can be obtained.

The invention relates to a fig health status assessment method and system based on multi-modal data fusion, and the method comprises the steps: constructing a multi-modal feature vector through fusing the features of a multi-spectral image, a hyperspectral cube and a visible light image; then proposing a multi-modal fusion suitability evaluation model, pre-processing the multi-modal feature vector, and then carrying out deep fusion and feature association to obtain a fusion feature; and finally, according to the fusion features, outputting an adaptation score and disease and pest classification. Therefore, the characteristic representation integrity is obviously enhanced, the evaluation accuracy and the pest and disease classification precision are improved, and the robustness and the anti-interference capability are enhanced.

The invention discloses a bridge apparent disease recognition method, which comprises the following steps of: 1, acquiring a two-dimensional image sample of a bridge apparent disease, marking a disease type according to a preset disease classification standard, and constructing an initial image data set; step 2, labeling the initial image data set to generate a label data set, and dividing the label data set into a training set, a verification set and a test set according to a ratio of 7: 2: 1; step 3, constructing a CSW-YOLO v9 model on the basis of a YOLOv9-m model architecture; 4, configuring hyper-parameters of the CSW-YOLO v9 model, performing iterative training by using the training set, performing performance verification through the verification set, and finally generating an optimized weight file; and step 5, inputting a to-be-detected bridge image into the CSW-YOLO v9 model loaded with the weight file, and outputting an apparent disease type and position information. The bridge apparent disease identification method provided by the invention is suitable for intelligent detection work of bridge multi-disease and small-target feature tasks.

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.

The invention relates to a multi-modal multi-task fusion attention model for Alzheimer's disease classification, and belongs to the technical field of attention model construction, and the multi-modal multi-task fusion attention model comprises a feature extraction module, a clinical guidance attention module, a proxy attention mechanism module, a dynamic gating feature fusion module and a multi-task loss function module which are connected in sequence. The agent attention mechanism module is also connected with the deep cross network module and the contrast learning projection module. According to the model, efficient fusion of image-clinical data is realized through a clinical attention guiding module, a dynamic gating feature fusion module and a multi-task comparison decoupling three-stage collaborative mechanism. The multi-modal multi-task fusion attention model is trained and verified based on 912 multi-center data of an ADNI database, the AD / CN binary classification accuracy is 100%, the AD / MCI binary classification accuracy is 95.16%, the AD / CN / MCI ternary classification accuracy is 92.47%, and the advanced level is achieved.

The invention provides a mental disorder aided decision-making method and system based on multi-modal data, and belongs to the technical field of disease aided decision-making, the method is applied to a system comprising a data acquisition module, a preliminary screening module and an aided decision-making module, and the method specifically comprises the following steps: preprocessing the multi-modal data of a patient; in combination with the mental disorder risk level of the patient of the preliminary screening model, decision assistance is triggered according to the mental disorder risk level, or corresponding decision suggestions are matched and output; when decision assistance is triggered, the disease classification probability and severity are obtained through a diagnosis model based on a cross-modal attention mechanism, meanwhile, according to a time-dependent risk prediction model, a survival probability curve of a recurrence risk is generated in combination with historical diagnosis data of a patient, and decision assistance suggestions are determined by integrating outputs of the two models. According to the method, on the basis of multi-modal data, multiple types of intelligent models are fused, objective data support is provided in the assessment and intervention process, and the missed diagnosis and misdiagnosis risks of mental disorders are reduced.

The invention provides an Alzheimer's disease prediction method based on incomplete modal comparative learning, and belongs to the technical field of multi-modal medical data classification. According to the method, an Alzheimer's disease prediction model is trained based on an Alzheimer's disease Obtaining an Alzheimer's disease classification result by using the trained Alzheimer's disease prediction model; the Alzheimer's disease prediction model comprises the following steps: extracting three-dimensional magnetic resonance imaging features and three-dimensional PET image features by using Swin Transform; obtaining structured data features based on the demographic data; splicing the three-dimensional magnetic resonance imaging features, the three-dimensional PET image features and the structured data features to obtain a comprehensive feature vector; and based on the comprehensive feature vector, carrying out classification through Transform to obtain an Alzheimer's disease classification result. Extracting spatial structure features and structured attribute information through a feature extractor; the similarity relation of the positive and negative sample pairs in a geometric space is optimized through a geometric contrast loss module, and the feature robustness and the discrimination capability under the condition of mode deficiency are improved.