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

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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

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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"

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