Journal of Jilin University(Engineering and Technology Edition) ›› 2020, Vol. 50 ›› Issue (5): 1747-1754.doi: 10.13229/j.cnki.jdxbgxb20191182

Previous Articles    

Improved image recognition algorithm based on multi⁃scale information fusion

Xiang-jiu CHE(),You-zheng DONG   

  1. College of Computer Science and Technology, Jilin University, Changchun 130012
  • Received:2019-12-24 Online:2020-09-01 Published:2020-09-16

RICH HTML

 20   

PDF (PC)

409

Abstract:

For the problem that there are many noise elements in the medical image samples and the distinction between positive and negative samples is not obvious, this paper designed a progressive type of multi-scale information fusion system, an end-to-end image automatic recognition model is established based on the existing convolution neural network structure with the attention mechanism in the classification of natural images. The model selected the corresponding fusion strategy for the output of different layers of the convolutional neural network and comprehensively utilized the information of different layers of the image. The improved model was used to identify the X-ray images of pneumonia patients and normal human beings, with an accuracy rate of 95.11%, an precision rate of 90.75%, and a recall rate of 90.28%, then a multi-group cross-contrast experiment was designed. Experimental results show that the improved model is superior in time and space complexity and the multi-scale information fusion mechanism designed in this paper improves the accuracy of image recognition tasks.

Key words: computer application technology, convolutional neural network, neumonia disease identification, attentional mechanism, multi-scale information fusion mechanism

CLC Number: 

  • TP391.4

Fig.1

ResNet residuals block structure diagram"

Fig.2

A single SENet unit of attention"

Fig.3

Modified building block"

Fig.4

Model structure diagram"

Fig.5

Fusion algorithm diagram"

Table 1

Residual network parameters"

名称Resnet-18结构Resnet-34结构
Conv_1

Conv 7×7

Max Pool 3×3

Conv 7×7

Max Pool 3×3

Conv_2Building block ×2Building block ×3
Conv_3Building block ×2Building block ×4
Conv_4Building block ×2Building block ×6
Conv_5Building block ×2Building block ×3
Downsample

Max Pool 2×2

BN，Relu，Conv 1×1

Max Pool 2×2

BN，Relu, Conv 1×1

AggregationAddAdd

Table 2

Resnet-18 experimental results"

网络类型准确率/%查准率/%查全率/%
VGG-1689.6582.4383.36
Resnet-1892.2585.6184.54
Resnet-18-A94.4489.2689.13
Resnet-18-B94.4889.0389.44
Resnet-18-C94.5990.3588.76

Table 3

Resnet-34 experimental results"

网络类型准确率/%查准率/%查全率/%
VGG-1689.6582.4383.36
Resnet-3493.2186.7386.86
Resnet-34-A94.0487.8488.84
Resnet-34-B95.0390.0890.55
Resnet-34-C95.1190.7590.28

Fig.6

Comparison diagram of model parameters and accuracy"

Fig.7

Comparison diagram of model calculation quantity and accuracy"

Fig.8

Positive sample identification"

Fig.9

Negative sample identification"

1 Krizhevsky A, Sutskever I,Hinton G. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6)： 84-90.
2 Kawahara J, Hamarneh G. Multi-resolution-tract cnn with hybrid pretrained and skin-lesion trained layers[C]∥Medical Image Computing and Computer-Assisted Intervention Workshop on Machine Learning in Medical Imaging. Springer International Publishing, 2016: 164-171.
3 Shen W, Zhou M, Yang F, et al. Multi-scale convolutional neural networks for lung nodule classification[J]. Inf Process Med Imaging, 2015, 24: 588-599.
4 Lin T Y, Dollár P, Girshick R, et al. Feature pyramid networks for object detection[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,USA, 2017: 2117-2125.
5 Liu W, Anguelov D, Erhan D, et al. Ssd: single shot multibox detector[C]∥European Conference on Computer Vision， Springer, Cham, 2016: 21-37.
6 Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
7 Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]∥Proceedings of the IEEE Cconference on Computer Vision and Pattern Recognition, USA, 2015: 1-9.
8 He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]∥Proceedings of the IEEE Cconference on Computer Vision and Pattern Recognition, USA, 2016: 770-778.
9 He K, Zhang X, Ren S, et al. Identity mappings in deep residual networks[C]∥European Conference on Computer Vision， Springer, Cham, 2016: 630-645.
10 Bell S, Lawrence Z C, Bala K, et al. Inside-outside net: detecting objects in context with skip pooling and recurrent neural networks[C]∥Proceedings of the IEEE Cconference on Computer Vision and Pattern Recognition, USA, 2016: 2874-2883.
11 Jaderberg M, Simonyan K, Zisserman A. Spatial transformer networks[C]∥Advances in Neural Information Processing Systems, Cambridge, MA, 2015: 2017-2025.
12 Hu J,Shen L, Sun G. Squeeze-and-excitation networks[C]∥Proceedings of the IEEE Cconference on Computer Vision and Pattern Recognition, USA, 2018: 7132-7141.
13 Zeiler M D, Fergus R. Visualizing and understanding convolutional networks[C]∥European Conference on Computer Vision， Springer, Cham, 2014: 818-833.
14 Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]∥Proceedings of the IEEE Cconference on Computer Vision and Pattern Recognition, USA, 2015: 3431-3440.
[1] Hong-wei ZHAO,Peng WANG,Li-li FAN,Huang-shui HU,Ping-ping LIU. Similarity retention instance retrieval method [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(6): 2045-2050.
[2] Xiang-jiu CHE,Hua-luo LIU,Qing-bin SHAO. Fabric defect recognition algorithm based onimproved Fast RCNN [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(6): 2038-2044.
[3] Zi-ji MA,Hao LU,Yan-ru DONG. Dual path convolutional neural network forsingle image super⁃resolution [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(6): 2089-2097.
[4] XU Yan,SUN Mei-shuang. Enhancing underwater image based on convolutional neural networks [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1895-1903.
[5] WANG Fang-shi, WANG Jian, LI Bing, WANG Bo. Deep attribute learning based traffic sign detection [J]. 吉林大学学报(工学版), 2018, 48(1): 319-329.
[6] MA Miao, LI Yi-bin. Multi-level image sequences and convolutional neural networks based human action recognition method [J]. 吉林大学学报(工学版), 2017, 47(4): 1244-1252.
[7] LI Lin-hui, LUN Zhi-mei, LIAN Jing, YUAN Lu-shan, ZHOU Ya-fu, MA Xiao-yi. Convolution neural network-based vehicle detection method [J]. 吉林大学学报(工学版), 2017, 47(2): 384-391.
[8] HU Guan-yu, QIAO Pei-li. High dimensional differential evolutionary algorithm based on cloud population for network security prediction [J]. 吉林大学学报(工学版), 2016, 46(2): 568-577.
[9] TONG Jin, WANG Ya-hui, FAN Xue-mei, ZHANG Shu-jun, CHEN Dong-hui. Monitoring system of cold chain logistics for farm fresh produce [J]. 吉林大学学报(工学版), 2013, 43(06): 1707-1711.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd