3 results about "Diagnostic model" patented technology

The invention discloses a nasopharyngeal carcinoma auxiliary diagnosis model construction and auxiliary diagnosis method and system, and relates to the technical field of medical data processing. Thenasopharyngeal carcinoma auxiliary diagnosis model construction method comprises the steps: sample acquiring: acquiring a nasal endoscope image, wherein the nasal endoscope image comprises a nasopharyngeal carcinoma group and a non-nasopharyngeal carcinoma group; preprocessing: preprocessing the nasal endoscope image; and model training: inputting the preprocessed nasal endoscope image into a convolutional neural network, and training the convolutional neural network to obtain a nasopharyngeal carcinoma auxiliary diagnosis model. According to the invention, the nasopharyngeal endoscope image can be analyzed, and the predicted illness probability is output in real time to assist a doctor in nasopharyngeal carcinoma diagnosis, so that the accuracy of nasopharyngeal carcinoma diagnosis can beeffectively improved, and the biopsy detection rate is increased so as to achieve the purposes of early screening, early diagnosis and early treatment of nasopharyngeal carcinoma and improvement of treatment effect and prognosis of patients.

The invention provides a disease diagnosis device and method based on medical images. The device comprises a feature extraction module used for performing feature extraction on an input medical image by using a convolutional neural network; the first diagnosis module is used for performing disease diagnosis reasoning based on the Bayesian network; the second diagnosis module is used for performing disease diagnosis and attribute category reasoning based on the graph convolutional network; and the diagnosis result fusion module is used for fusing the output of the first diagnosis module and the output of the second diagnosis module to obtain a final diagnosis result. According to the invention, disease diagnosis reasoning is carried out based on the Bayesian network and the graph convolution network, so that the disease intelligent diagnosis model can think like professional doctors and deduce diseases from imaging characterization of lesions, verifiable disease intelligent diagnosis is realized, professional knowledge of the doctors is fused, and the diagnosis accuracy is improved. Therefore, the accuracy, verifiability and generalization of automatic diagnosis are improved.