Classification of pulmonary lesions based on CNN and chest X-ray images

Abstract

This article mainly introduces the convolutional neural network (CNN) and uses CNN to realize the processing and classification prediction of chest X-ray images (CXR), to determine whether the lung has lesions, and finally the final AUC score of 0.85556 through CNN. In order to further improve the accuracy, after referring to many documents and considering the actual situation, I chose to perform principal component analysis (PCA) in the image preprocessing part, replace the random initial sample with the principal component initial sample, and replace the random initial kernel with the principal component initial kernel. To avoid staying in the local optimum when stochastic gradient descent finds the optimal kernel. The PCA + CNN model predicted an AUC score of 0.89333, an increase of 0.03777.

Identification of Pneumonia in Chest X-Ray Image Based on Transformer

“…e model was tested with a higher accuracy score than four human medical imaging experts correctly judged [11]. Later, many scholars further improved the models based on the convolutional neural network according to the features of CXR [12][13][14][15][16][17][18][19][20]. But accuracy of models began to encounter bottlenecks, and there are still some unsolved or imperfect problems in the current models.…”