Children brain MR image viral encephalitis classification system

Abstract

A children brain MR image viral encephalitis classification system comprises a computer memory, a computer processor and a computer program which is stored in the computer memory and can be executed on the computer processor, and a trained classification model is stored in the computer memory. The classification model adopts an improved SE ResNet network model and comprises four convolution parts, each convolution part is composed of a plurality of sub-module groups, each sub-module group comprises an Inception sub-module and an SE Res sub-module, and finally, a final classification result is obtained through a full connection layer; and when the computer processor executes the computer program, the following steps are implemented: inputting a to-be-classified child brain MR image into a trained classification model to obtain a viral encephalitis classification result. By means of the method, the learning ability of the model for different dimension features can be improved, and the efficiency and accuracy of children viral encephalitis diagnosis are greatly improved.

Description

technical field [0001] The invention belongs to the field of medical artificial intelligence, and in particular relates to a classification system for viral encephalitis in children's brain MR images. Background technique [0002] Encephalitis in children is a relatively common disease in pediatrics. In general, a comprehensive judgment can be made through clinical symptoms, laboratory tests, and imaging and EEG tests. If diagnosed, the patient needs targeted treatment under the guidance of a professional doctor. [0003] At present, doctors diagnose mainly through clinical symptoms, laboratory examinations (cerebrospinal fluid examination), imaging and EEG testing, etc. However, clinical symptoms are not very accurate; imaging and EEG testing can only be observed with the naked eye in severe cases Lesion area; CSF examination is more accurate, but it takes a long time, and needs to extract CSF, causing trauma and pain to children. [0004] With the development of artific...

AI Technical Summary

Problems solved by technology

[0007] However, for children with viral encephalitis, the characteristics of imaging data are not obvious, and it is difficult to accurately diagnose whether children have viral encephalitis using conventional deep learning methods

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