Pneumonia Recognition Based on Convolutional Neural Network Feature Map Fusion

• Published in: Journal of physics. Conference series 2021-01, Vol.1757 (1), p.12047
• Main Authors: Liu, Xiaozhong, Wang, Zaixing, Zheng, Lijun, Gao, Jinhui
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial neural networks; Convolutional Neural Network; Feature Map Fusion; Feature maps; Feature recognition; Neural networks; Physics; Pneumonia; Pneumonia Image Recognition
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/1757/1/012047

Pneumonia recognition has important research significance in computer-aided diagnosis, and there is a problem of low accuracy for pneumonia recognition. In this paper, an improved network is based on the convolutional neural network AlexNet, the AlexNet_Branch network. The AlexNet_Branch network adds a parallel branch convolutional neural network to AlexNet, and it connects AlexNet and the branch convolutional neural network at the fully connected layer. During training, the same image is simultaneously obtained by AlexNet and the branch convolutional neural network to obtain different feature maps, and then the feature maps are merged together at the fully connected layer to improve the accuracy of recognition. Through design experiments, different AlexNet_Branch networks composed of different layers of branch convolutional neural networks were built, and the network was trained and tested on the chest X-ray image set respectively. The results show that the addition of a branch convolutional neural network greatly improves the accuracy of pneumonia recognition, and the AlexNet_Branch network test accuracy consisting of a 16-layer branch convolutional neural network is 98.01%.