Research on Chest X-ray Image Recognition and Classification Model based on Deep Learning
Objectives Building a chest X-ray(CXR)image classification model based on convolutional neural networks in deep learning,providing reliable auxiliary diagnostic techniques for lung diseases.Methods Four kinds of chest X-ray pictures of COVID-19,mild pulmonary infection,viral pneumonia and normal were collected through KAGGLE database,and the data were randomly divided into training set,test set and verification set according to 3∶1∶1 ratio.Building a CXR image classification model based on convolutional neural network architecture,adjusting hyperparameters to strengthen and optimize the model.Subsequently,the model was validated and evaluated using metrics such as confusion matrix,accuracy,sensitivity,and K-fold cross validation results.Results The classification accuracy of this research model for lung imaging images is 0.81,the sensitivity is 0.80,and the loss values of the test and validation sets can be stable at a relatively low level.Compared with models with the same migration algorithm,the accuracy,sensitivity,and F1 score on the test dataset were improved by 1.7%,1.7%,1.3%,and 2.9%,respectively.Conclusion This model has stronger recognition and classification performance for CXR images,and can be more effectively applied to auxiliary analysis and judgment of lung diseases.
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