Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (11): 2653-2661.doi: 10.13229/j.cnki.jdxbgxb20210347

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Brain tumor image classification based on improved residual capsule network and sparrow search

Sheng-sheng WANG1(),Chen-xu LI1,Xiang-yu WANG1,Zhi-lin YAO1(),Yi-shen LIU2,Jia-qian WU2,Qing-ran YANG2   

  1. 1.College of Computer Science and Technology,Jilin University,Changchun 130012,China
    2.High School Attached to Northeast Normal University,Changchun 130021,China
  • Received:2021-04-22 Online:2022-11-01 Published:2022-11-16
  • Contact: Zhi-lin YAO E-mail:wss@jlu.edu.cn;yaozl@jlu.edu.cn

Abstract:

A magnetic resonance imaging(MRI) brain tumor image classification method based on sparrow search algorithm and scaled reconstruction residual capsule network was proposed. Firstly, for MRI brain tumor images with poor image quality, an image enhancement method based on sparrow search was taken to improve the image quality. Secondly, the capsule network was used to achieve better results on the small data volume and unbalanced medical dataset. Finally, in view of the gradient disappearance and gradient explosion problems of the capsule network for large size images, an improved residual network was used to extract the key features of large size images. Meanwhile, by using scaled reconstruction, the volume of model decreased while the calculation speed increased. The experimental results verify the effectiveness of the proposed method in the classification of small samples, low-quality, large size MRI brain tumor images.

Key words: computer application technology, brain tumor classification, sparrow search algorithm, residual network, capsule network, dynamic routing agreement

CLC Number: 

  • TP399

Table1

Three network structures"

CapsuleRescap less heightRescap less layer
Input(224×224×1, image)Input(112×112×4, image)Input(224×224×1, image)
8×8 Conv,256,stride 83×3 Conv,643×3 Conv,64
3×3conv,643×3conv,64×23×3conv,323×3conv,32×2

3×3conv,1283×3conv,128

1×1 Conv,128,stride 2

3×3conv,643×3conv,64×2
3×3conv,1283×3conv,128

3×3conv,2563×3conv,256

1×1 Conv,256,stride 2

3×3conv,1283×3conv,128

1×1 Conv,128,stride 2

3×3conv,2563×3conv,2563×3conv,1283×3conv,128

3×3conv,5123×3conv,512

1×1 Conv,512,stride 2

3×3conv,2563×3conv,256

1×1 Conv,256,stride 2

3×3conv,5123×3conv,5123×3conv,2563×3conv,256
特征图reshape为(28×28×128)
8D/3200 primary capsule8D/288 primary capsule8D/288 primary capsule
16D/4 final capsule16D/4 final capsule16D/4 final capsule
Dense 512Dense 512Dense 512
Dense 1024Dense 1024Dense 1024
Dense 224×224Dense 784Dense 784

Fig.1

Samples of each category in the original dataset"

Fig.2

Original images and images with 10% SPN"

Table2

Introduction of dataset"

数据类别glioma tumormeningioma tumorpituitary tumor

no

tumor

Total
训练集8268228273952870
测试集10011574105394
总实验数据集9269379015003264

Table 3

Comparison of training results of three models"

模型LossAccuracy/%Parameter
Rescap less layer0.301373.108 512 948
Rescap less height0.304772.8414 780 084
Capsule0.317968.278 327 472

Table 4

Comparison of classification results of three models"

类别Rescap less layerRescap less heightCapsule
Sensitivity/%Specificity/%Sensitivity/%Specificity/%Sensitivity/%Specificity/%
No tumor100.0078.89100.0076.4797.1476.47
Meningioma tumor96.5287.8195.6588.8986.0984.95
Glioma tumor19.00100.0020.00100.0018.0099.32
Pituitary tumor71.6296.5670.2797.5067.5795.94

Table 5

Comparison with other methods"

模型Accuracy/%
不使用图像增强使用麻雀搜索 图像增强
VGG1945.6853.86
ResNet1858.1264.38
文献[1461.7069.50
文献[1563.8070.30
Rescap less layer68.5073.10
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