The invention discloses an enteroscope Crohn's disease auxiliary diagnosis system based on deep learning, and the system comprises: an image collection module which is connected to an endoscope host through a collection card, and obtains each frame of image information collected by the endoscope host; an image preprocessing module which is configured to preprocess the acquired image information; an auxiliary diagnosis model which is configured to carry out Crohn's disease area identification on the input preprocessed image after being subjected to optimization training through the training set, wherein the auxiliary diagnosis model comprises an image input layer, a convolution layer, a spatial pyramid pooling module and a full connection layer which are connected in sequence. According tothe method, the spatial pyramid pooling module is added between the convolution layer and the full connection layer, so that the feature map of any size can be converted into the feature vector of a fixed size, balanced identification of a small target and a large target is realized, and the accuracy of the model can be improved.

Enteroscopy Crohn's disease auxiliary diagnosis system based on deep learning

The invention provides an ulcerative colitis severity assessment method and system based on deep learning The method comprises the following steps: marking a Mayo endoscopic score and an ulcerative colitis endoscopic activity index score on a white light colonoscope image to form a sample set; constructing an ulcerative colitis severity evaluation model, and performing training by using the marked white light colonoscope image sample in the sample set, wherein the ulcerative colitis severity evaluation model is an SPPNet network with a characteristic pyramid pooling function, and the output of the ulcerative colitis severity evaluation model comprises four softmax functions; receiving a white light colonoscope image in real time; and outputting score prediction results of Mayo endoscopicscores, vascular typing, spontaneous hemorrhage and erosion ulcer characteristics by utilizing the ulcerative colitis severity degree evaluation model, and accumulating the score prediction results ofthe vascular typing, spontaneous hemorrhage and erosion ulcer characteristics to obtain an ulcerative colitis endoscopic activity index score The method can be used for automatically grading inflammation, can meet the grading requirements of two clinical most common UC severity degrees at the same time, and is high in result accuracy and good in repeatability

Ulcerative colitis severity assessment method and system based on deep learning

A video acquisition system for a narrow cavitary space based on a microprobe structure, comprising an intelligent computer aided diagnosis system and a microprobe (104). The intelligent computer aided diagnosis system trains a large number of pictures of different lesions using a deep learning technique, and can complete auxiliary diagnosis of different common lesions in the colonoscopy process. The microprobe (104) is provided at the tail end of a clamp channel (103) for taking samples from tissues. When the intelligent computer aided diagnosis system determines that some of the tissues taken out are pathological tissues, the microprobe (104) is retracted from the clamp channel (103) and carries the pathological tissues out of a colonoscope (102) duct from the clamp channel (103). When it is determined that the some of the tissues taken out are normal intestinal tissues or intestinal debris, the microprobe (104) discharges the tissues and is not retracted. The video acquisition system can reduce the probability of missed detection of lesions.

Video acquisition system for narrow cavitary space based on microprobe structure

The invention provides an auxiliary display system of a photographing device. The photographing device performs photographing in human body and generates image information. The auxiliary display system of the photographing device comprises the components of a service terminal which comprises an algorithm analysis module that is used for successively performing identification analysis on image information in real time and generates suspected focus information; and a client which is in communication connection with the service terminal and comprises a displaying control module, wherein the displaying control module is used for receiving the suspected focus information transmitted from the algorithm analysis module, matching the corresponding image information and the suspected focus information and performing real-time displaying. After the algorithm analysis module acquires the suspected focus information in real time through analysis, the displaying control module acquires and displaysthe suspected focus information and the related image information in real time. Therefore, an operator can judge the image information according to the suspected focus information, thereby reducing mis-examinations and improving examination efficiency.

Auxiliary display system for photographing device

A medical endoscope image identification method and system, and equipment and an endoscope image system. The method comprises: obtaining an original endoscope image according to a medical endoscope video stream (310); filtering the original endoscope image via a neural network to generate a target endoscope image (330); identifying, via the neural network, organ information corresponding to the target endoscope image (350); according to the corresponding organ information, identifying, via a classification network, an image type applicable to the target endoscope image (370); and in a photography mode corresponding to the image type, locating a lesion area in the target endoscope image according to the part indicated by the organ information, and identifying a lesion category to which the lesion area belongs (390). The method achieves high robustness, and achieves classification prediction for the whole process of the photography of an endoscope, thereby achieving systematic and complete image identification.

Medical endoscope image identification method and system, and endoscope image system

The invention discloses an enteroscopy Crohn's disease auxiliary diagnosis system and method based on deep learning. The system comprises an enteroscopy image automatic acquisition subsystem, a database, a client and a server. The enteroscope image automatic acquisition subsystem is used for acquiring an enteroscope image; wherein the database comprises a sample set for training the convolutionalneural network; and the client is used for uploading the enteroscopy image acquired by the enteroscopy image automatic acquisition subsystem to the server, and receiving and displaying an analysis result fed back by the server. According to the method, an image recognition technology is utilized to monitor an endoscope video in real time, images containing key organ parts and suspicious Crohn focus areas are automatically collected, and after global preferences are made according to a weighting algorithm, whether the image is Crohn disease or not is diagnosed. According to the method, after the neural network model is utilized to automatically screen the images, the most valuable image can be extracted from the global video, auxiliary diagnosis is given, and more reliable and efficient support is provided for doctors to diagnose.

Enteroscopy Crohn's disease auxiliary diagnosis system and method based on deep learning

The invention discloses an auxiliary diagnosis system and method for ulcerative colitis under enteroscopy based on deep learning. The system comprises an enteroscopy image automatic acquisition subsystem, a client, a server and a database. The enteroscope image automatic acquisition subsystem is used for acquiring an enteroscope image; The client is used for uploading the enteroscopy image acquired by the enteroscopy image automatic acquisition subsystem to the server, and judging whether the image is qualified or not and whether the image comprises ulcerative colitis judgment or not, and an analysis result fed back by the server is received and displayed; the database is used for storing the sample set for training the convolutional neural network, the acquired enteroscopy image and the analyzed and output information. According to the method, an image recognition technology is utilized to monitor an endoscope video in real time, images containing key organ parts and suspicious focusareas are automatically collected, a neural network model is utilized to automatically screen the images, the most valuable image can be extracted from a global video, and more reliable and efficientsupport is provided for doctors to diagnose.

An auxiliary diagnosis system and method for ulcerative colitis under enteroscopy based on deep learning

The invention discloses an enterophthisis auxiliary diagnosis system and method based on deep learning. The system comprises a colonoscopy image automatic collection subsystem, a client and a database. The colonoscopy image automatic collection subsystem is used for collecting colonoscopy images, and the client includes an image preprocessing module, a convolutional neural network module, an imagedisplay module, and a colonoscopy report output module. The convolutional neural network module comprises an image eligibility discriminant submodel, an image part discriminant submodel, and a discrimination submodule for determining whether an image contains a focus of infection. The database is used for saving a sample set for training the convolutional neural network, and storing the information outputted by the colonoscopy report output module. The system utilizes image recognition technology to monitor an endoscope video in real time, automatically collects images containing key organ parts and suspicious lesion areas, and saves them in the database after global selection according to a weighting algorithm. The system can extract the most valuable images from the global video and provide more reliable and efficient support for doctor diagnosis.

Enterophthisis auxiliary diagnosis system and method based on deep learning

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Enteroscopy Crohn's disease auxiliary diagnosis system and method based on deep learning

An auxiliary diagnosis system and method for ulcerative colitis under enteroscopy based on deep learning

Enterophthisis auxiliary diagnosis system and method based on deep learning