基于改进残差胶囊网络和麻雀搜索的脑瘤图像分类

doi:10.13229/j.cnki.jdxbgxb20210347

Abstract

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

Sheng-sheng WANG,Chen-xu LI,Xiang-yu WANG,Zhi-lin YAO,Yi-shen LIU,Jia-qian WU,Qing-ran YANG. Brain tumor image classification based on improved residual capsule network and sparrow search[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(11): 2653-2661.

Figures/Tables 7

Table1

Three network structures"

Capsule	Rescap less height	Rescap less layer
Input（224×224×1， image）	Input（112×112×4， image）	Input（224×224×1， image）
8×8 Conv，256，stride 8	3×3 Conv，64	3×3 Conv，64
	$3 × 3 c o n v, 64 3 × 3 c o n v, 64 × 2$	$3 × 3 c o n v, 32 3 × 3 c o n v, 32 × 2$
	$3 × 3 c o n v, 128 3 × 3 c o n v, 128$ 1×1 Conv，128，stride 2	$3 × 3 c o n v, 64 3 × 3 c o n v, 64 × 2$
	$3 × 3 c o n v, 128 3 × 3 c o n v, 128$	$3 × 3 c o n v, 64 3 × 3 c o n v, 64 × 2$
	$3 × 3 c o n v, 256 3 × 3 c o n v, 256$ 1×1 Conv，256，stride 2	$3 × 3 c o n v, 128 3 × 3 c o n v, 128$ 1×1 Conv，128，stride 2
	$3 × 3 c o n v, 256 3 × 3 c o n v, 256$	$3 × 3 c o n v, 128 3 × 3 c o n v, 128$
	$3 × 3 c o n v, 512 3 × 3 c o n v, 512$ 1×1 Conv，512，stride 2	$3 × 3 c o n v, 256 3 × 3 c o n v, 256$ 1×1 Conv，256，stride 2
	$3 × 3 c o n v, 512 3 × 3 c o n v, 512$	$3 × 3 c o n v, 256 3 × 3 c o n v, 256$
－	特征图reshape为（28×28×128）	－
8D/3200 primary capsule	8D/288 primary capsule	8D/288 primary capsule
16D/4 final capsule	16D/4 final capsule	16D/4 final capsule
Dense 512	Dense 512	Dense 512
Dense 1024	Dense 1024	Dense 1024
Dense 224×224	Dense 784	Dense 784

Table1

Fig.1

Fig.2

Table2

Table 3

Table 4

Table 5

References 15

1	Pan Y, Huang W, Lin Z, et al. Brain tumor grading based on neural networks and convolutional neural networks[C]∥37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Milan, Italy, 2015: 699-702.
2	Deepak S, Ameer P M. Brain tumor classification using deep CNN features via transfer learning[J]. Computers in Bbiology and Medicine, 2019, 111: 103345.
3	Navid G, Afshin S, Modjtaba R.Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images[J]. Biomedical Signal Processing and Control, 2020, 57: 101678.
4	Javaria A, Muhammad S, Nadia G, et al. Brain tumor classification based on DWT fusion of MRI sequences using convolutional neural network[J]. Pattern Recognition Letters, 2020, 129: 115-122.
5	Anupama M A, Sowmya V, Soman K P. Breast cancer classification using capsule network with preprocessed histology images[C]∥The 8th International Conference on Communication and Signal Processing, Melmaruvathur, India, 2019: 143-147.
6	Afshar P, Oikonomou A, Naderkhani F, et al. 3D-MCN: a 3D multi-scale capsule network for lung nodule malignancy prediction[J]. Scientific Reports, 2020, 10(1): 1-11.
7	Afshar P, Heidarian S, Naderkhani F, et al. Covid-caps: a capsule network-based framework for identification of covid-19 cases from x-ray images[J]. Pattern Recognition Letters, 2020, 138: 638-643.
8	Xue J K, Shen B. A novel swarm intelligence optimization approach: sparrow search algorithm[J]. Systems Science & Control Engineering An Open Access Journal, 2020, 8(1): 22-34.
9	Chen J, Yu W, Tian J, et al. Image contrast enhancement using an artificial bee colony algorithm[J]. Swarm and Evolutionary Computation, 2018, 38: 287-294.
10	Sabour S, Frosst N, Hinton G E. Dynamic routing between capsules[C]∥The 31st Annual Conference on Neural Information Processing Systems, Long Beach, CA, USA, 2017: 3857-3867.
11	He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 770-778.
12	许骞艺, 秦贵和, 孙铭会, 等. 基于改进的ResNeSt驾驶员头部状态分类算法[J]. 吉林大学学报: 工学版, 2021, 51(2): 704-711.
	Xu Qian-yi, Qin Gui-he, Sun Ming-hui, et al. Classification of drivers' head status based on improved ResNeSt[J].Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 704-711.
13	方明, 陈文强. 结合残差网络及目标掩膜的人脸微表情识别[J]. 吉林大学学报: 工学版, 2021, 51(1): 303-313.
	Fang Ming, Chen Wen-qiang. Face micro-expression recognition based on ResNet with object mask[J].Journal of Jilin University(Engineering and Technology Edition), 2021, 51(1): 303-313.
14	Minarno A E, Mandiri M H C, Munarko Y, et al. Convolutional neural network with hyperparameter tuning for brain tumor classification[J]. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics and Control, 2021, 6(2): 127-132.
15	Jaeyong K, Zahid U, Jeonghwan G. MRI-based brain tumor classification using ensemble of deep features and machine learning classifiers[J]. Sensors, 2021, 21(6): 21062222.

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模型	Loss	Accuracy/%	Parameter
Rescap less layer	0.3013	73.10	8 512 948
Rescap less height	0.3047	72.84	14 780 084
Capsule	0.3179	68.27	8 327 472

类别	Rescap less layer		Rescap less height		Capsule
类别	Sensitivity/%	Specificity/%	Sensitivity/%	Specificity/%	Sensitivity/%	Specificity/%
No tumor	100.00	78.89	100.00	76.47	97.14	76.47
Meningioma tumor	96.52	87.81	95.65	88.89	86.09	84.95
Glioma tumor	19.00	100.00	20.00	100.00	18.00	99.32
Pituitary tumor	71.62	96.56	70.27	97.50	67.57	95.94

模型	Accuracy/%
模型	不使用图像增强	使用麻雀搜索图像增强
VGG19	45.68	53.86
ResNet18	58.12	64.38
文献［14］	61.70	69.50
文献［15］	63.80	70.30
Rescap less layer	68.50	73.10

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

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