Classification of Pancreatic Cystic Tumors Based on DenseNet and Transfer Learning

doi:10.11938/cjmr20223047

Abstract

Abstract:

This work applied the classification model of DenseNet combined with transfer learning to classify mucinous cystic tumor (MCN) from serous cystic tumor (SCN) of the pancreas. Firstly, the data of 65 MCNs and 107 SCNs from Changhai Hospital were augemented and preprocessed. Secondly, the classification model of DenseNet combined with transfer learning was constructed and fine-tuned, MCN and SCN were classified by 5-fold cross validation, and the proposed classification model was compared with AlexNet, VGG16, ResNet50 and other deep learning models. The classification model in this paper yielded the best recognition effect. The area under the ROC curve (AUC value), accuracy rate, recall rate and precision rate of the test set were 0.989, 0.943, 0.949 and 0.938 respectively. It proved that the classification model based on DenseNet combined with transfer learning has higher recognition accuracy for MCN and SCN and stronger learning ability than other deep learning models, which can help doctors in clinical diagnosis, and save manpower and material resources. It further confirmed the potential value and clinical significance of this model for the classification of pancreatic cystic tumors.

Key words: deep learning, DenseNet, transfer learning, cystic tumor of pancreas

CLC Number:

TP394.1

TIAN Hui, WU Jie, BIAN Yun, ZHANG Zhiwei, SHAO Chengwei. Classification of Pancreatic Cystic Tumors Based on DenseNet and Transfer Learning[J]. Chinese Journal of Magnetic Resonance, 2023, 40(3): 270-279.

Figures/Tables 8

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Layers	DenseNet161
Convolution	7×7 conv
Pooling	3×3 max pool
Dense Block 1	$\left[ \begin{matrix} 1\times 1\ \text{conv} \\ 3\times 3\ \text{conv} \\ \end{matrix} \right]\times 6$
Transition Layer 1	1×1 conv, 2×2 average pool
Dense Block 2	$\left[ \begin{matrix} 1\times 1\ \text{conv} \\ 3\times 3\ \text{conv} \\ \end{matrix} \right]\times 12$
Transition Layer 2	1×1 conv, 2×2 average pool
Dense Block 3	$\left[ \begin{matrix} 1\times 1\ \text{conv} \\ 3\times 3\ \text{conv} \\ \end{matrix} \right]\times 36$
Transition Layer 3	1×1 conv, 2×2 average pool
Dense Block 4	$\left[ \begin{matrix} 1\times 1\ \text{conv} \\ 3\times 3\ \text{conv} \\ \end{matrix} \right]\times 24$
Classification Layer	7×7 global average pool, fully-connected, softmax

模型	ACC	AUC
DenseNet161	0.767	0.837
DenseNet161+迁移学习	0.943	0.989

模型	ACC	Precision	Recall	Specificity	F1-Score	AUC	参数量
AlexNet	0.844	0.846	0.842	0.847	0.844	0.930	6100840
MobileNet_v3	0.889	0.894	0.884	0.895	0.889	0.960	5483032
Vgg16	0.898	0.907	0.886	0.909	0.896	0.966	138365992
ResNet50	0.912	0.923	0.898	0.926	0.910	0.971	25557032
本文	0.943	0.938	0.949	0.937	0.943	0.989	28681000

	方法	数据	ACC
文献[7]	影像组学特征融合	胰腺囊性肿瘤CT图像	0.862
文献[8]	CT纹理特征模型+临床影像学特征模型	SCN与MCN静脉期CT图像	0.938 (AUC)
文献[9]	影像组学参数+人工神经网络	SCN与MCN CT图像	0.895
文献[12]	ResNet50+迁移学习	SCN与MCN内窥镜超声图像	0.828
本文	DenseNet161+迁移学习	SCN与MCN T₂加权MR图像	0.943