Nasopharyngeal carcinoma auxiliary diagnosis model construction and auxiliary diagnosis method and system

Abstract

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.

Description

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AI Technical Summary

Benefits of technology

This patented technology helps train deep learning models from images taken during medical procedures such as nonsurgery or surgery (NNS). It allows researchers to analyze patient samples with both normal and affected areas simultaneously without having to manually label them separately. By analyzing these images together, they are able to identify specific types of tumors associated with different causes - either alone or combined. They also suggest ways to diagnose other conditions like lung cancer based upon their symptoms. Overall this technology makes it possible to better detect and treat nasoepithelial carcinoid tumor cases faster than traditional methods due to its ability to learn patterns through training complex networks instead of just one single feature vector per pixel.

Problems solved by technology

This patent describes various technical techniques related to improving the quality of medical imagery obtained via computerized tomography scans. However, current systems require manual intervention and may result in errors caused by subjective factors like fatigue. There is also lack of automatic tools available at homecare centers where advanced technologies could help identify cancerous areas more accurately than traditional approaches.

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