Real-time auxiliary diagnosis and treatment system for nasal internal diseases based on Att-Res2-CE-Net

Abstract

The invention discloses an intra-nasal disease real-time auxiliary diagnosis and treatment system based on Att-Res2-CE-Net, based on the Att-Res2-CE-Net; the Res2Net50 feature is used for extracting the backbone network, wherein a dense cavity convolution block DAC module and a residual multi-core pooling RMP module are embedded into an Attention U-Net network. The system comprises a video acquisition processing end, a cloud platform and a server end, wherein the video collecting and processing end comprises a plurality of nasal endoscope video collecting devices and is in wireless connection with the cloud platform, the cloud platform is in wireless connection with the server, the system is ingenious in design and reasonable in structure, the disadvantages and advantage analysis of all networks are integrated, wherein the advantages are enhanced and the disadvantages are avoided. Through combination of software and hardware, a target segmentation network and a target tracking network can be established on the server, and the real-time operation video provided by the video acquisition and processing end is used for accurately identifying and positioning nasal polyp, fungal sinusitis and nasal cavity tumor. The system is low in cost, flexible and convenient to use and capable of assisting doctors in diagnosis and improving diagnosis efficiency.

Description

technical field [0001] The invention relates to the technical field of medical auxiliary diagnosis and treatment, in particular to a real-time auxiliary diagnosis and treatment system for internal nasal diseases based on Att-Res2-CE-Net. Background technique [0002] The clinical incidence of nasal cavity and sinus diseases is high, and the forms are diverse, especially sinusitis, nasal polyps and other diseases are more common. Although the range of patients with this type of disease is small, it is still a large category of health diseases that plague many people because of its special location and is closely related to people's lives. However, the complexity and hidden characteristics of the nasal cavity, sinuses and adjacent structures have caused great trouble to the doctor's diagnosis. [0003] On the one hand, the misdiagnosis rate of existing traditional diagnostic methods is high. According to the survey data, compared with other types of diseases, nasal cavity dis...

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Problems solved by technology

However, the complexity and deepness of the nasal cavity, sinuses and adjacent structures have caused great trouble to the doctor's diagnosis

[0003] On the one hand, the misdiagnosis rate of existing traditional diagnostic methods is high. According to the survey data, compared with other types of diseases, nasal cavity disease ranks one of the top in the misdiagnosis rate of the first diagnosis, and the average misdiagnosis rate of the first diagnosis is as high as 10%. On the other hand, although At present, the development of target detection and tracking algorithms based on deep learning has matured, but the application of artificial intelligence medical image-assisted diagnosis and treatment in the identification of nasal cavity diseases is still a relatively blank field, and technical support is urgently needed to improve the diagnostic accuracy of nasal cavity diseases

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