Research on Deep Learning Model of Feature Extraction Based on Convolutional Neural Network
This work addresses the challenge of deploying efficient deep learning models for pneumonia detection on resource-constrained terminals, representing an incremental improvement in medical image analysis.
This paper tackled the problem of accurately identifying pneumonia with deep learning models while reducing computational costs, achieving improvements in prediction accuracy, specificity, and sensitivity by 4.25, 7.85, and 2.32 percentage points, respectively, and reducing graphics processing usage by 51% compared to InceptionV3.
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.