A child pneumonia auxiliary diagnosis model and its training method

Abstract

The present invention provides an auxiliary diagnosis model for childhood pneumonia and a training method thereof. The training method includes acquiring medical images of children with pneumonia and corresponding medical diagnosis sentences, the medical images being used as a training image set, and the medical diagnosis sentences being used as training sentences ; Extract the image depth feature vector from the image training set data through CNN neural network to obtain a depth feature atlas, and perform word vector training on the training sentence through the word2vec model to obtain a depth feature vector word set; The feature fusion of the deep feature vector word set and the deep feature vector word set is carried out, and then the LSTM neural network is used for training, that is, a trained child pneumonia auxiliary diagnosis model can be obtained. The present invention trains the existing medical images of children with pneumonia and the corresponding medical diagnosis sentences, so that the model obtained by training can be used as a tool for doctors to learn diagnosis or provide effective reference opinions for doctors' clinical diagnosis.

Description

technical field [0001] The invention relates to the technical field of medical computers, in particular to a child pneumonia auxiliary diagnosis model and a training method thereof. Background technique [0002] Pneumonia is a pulmonary inflammation caused by pathogen infection or inhalation of amniotic fluid, oils, and allergic reactions. The main clinical manifestations are fever, cough, shortness of breath, dyspnea, and pulmonary rales. The examination of children's pneumonia includes routine blood examination, C-reactive protein test, etiological examination, and chest X-ray examination. At present, the diagnosis of pneumonia in children is generally based on physical signs and chest X-ray for detection and diagnosis. This method of diagnosis shows enhancement of lung markings in the early stage, and later, there are spot-like infiltration of different sizes in the middle and lower fields of both lungs, or fusion into a film. Shape shadow, often complicated by emphysema...

AI Technical Summary

Problems solved by technology

However, the interpretation results of the same chest X-ray medical image at different time points or by different doctors are highly inconsistent and have great observer differences

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