  Neural networks with relatively shallow layers and simple structures may have
limited ability in accurately identifying pneumonia. In addition, deep neural
networks also have a large demand for computing resources, which may cause
convolutional neural networks to be unable to be implemented on terminals.
Therefore, this paper will carry out the optimal classification of
convolutional neural networks. Firstly, according to the characteristics of
pneumonia images, AlexNet and InceptionV3 were selected to obtain better image
recognition results. Combining the features of medical images, the forward
neural network with deeper and more complex structure is learned. Finally,
knowledge extraction technology is used to extract the obtained data into the
AlexNet model to achieve the purpose of improving computing efficiency and
reducing computing costs. The results showed that the prediction accuracy,
specificity, and sensitivity of the trained AlexNet model increased by 4.25
percentage points, 7.85 percentage points, and 2.32 percentage points,
respectively. The graphics processing usage has decreased by 51% compared to
the InceptionV3 mode.
