| 基于STN与异构卷积滤波器的肝硬化识别 |
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| 引用本文： | 张欢,赵希梅. 基于STN与异构卷积滤波器的肝硬化识别[J]. 计算机工程, 2021, 47(5): 301-307,315. DOI: 10.19678/j.issn.1000-3428.0057341 |
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| 作者姓名： | 张欢 赵希梅 |
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| 作者单位： | 1. 青岛大学 计算机科学技术学院, 山东 青岛 266071;2. 山东省数字医学与计算机辅助手术重点实验室, 山东 青岛 266000 |
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| 摘 要： | 卷积神经网络因缺乏空间不变性造成分类精度不高,且由于复杂度过高导致分类效率较低.提出一种利用空间变换网络和异构卷积滤波器的SH_ImAlexNet网络,应用于肝硬化样本识别.改进卷积神经网络AlexNet的结构和参数以满足肝硬化样本尺度要求,引入空间变换网络层增强特征提取能力与空间不变性,采用异构卷积滤波器替换部分卷积...
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| 关 键 词： | 空间变换网络 异构卷积滤波器 AlexNet模型 卷积神经网络 肝硬化识别 |
| 收稿时间： | 2020-02-07 |
| 修稿时间： | 2020-03-31 |
Identification of Liver Cirrhosis Based on STN and Heterogeneous Convolution Filter |
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| ZHANG Huan,ZHAO Ximei. Identification of Liver Cirrhosis Based on STN and Heterogeneous Convolution Filter[J]. Computer Engineering, 2021, 47(5): 301-307,315. DOI: 10.19678/j.issn.1000-3428.0057341 |
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| Authors: | ZHANG Huan ZHAO Ximei |
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| Affiliation: | 1. College of Computer Science and Technology, Qingdao University, Qingdao, Shandong 266071, China;2. Shangdong Provincial Key Laboratory of Digital Medicine and Computer Aided Surgery, Qingdao, Shandong 266000, China |
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| Abstract: | The lack of space invariance of Convolutional Neural Network(CNN)results in low classification accuracy and low classification efficiency due to its high complexity.This paper proposes a SH_ImAlexNet network based on Spatial Transformer Network(STN)and Heterogeneous Convolution(HetConv)filter for the identification of liver cirrhosis samples.The structure and parameters of CNN AlexNet are optimized to fit into the size of liver cirrhosis samples,and the STN layer is introduced to enhance the feature extraction ability and spatial invariance.The HetConv filter is used to replace part of the convolution kernel to reduce the complexity and improve the robustness.Experimental results show that the classification performance of the proposed network is better than that of traditional networks such as AlexNet and VGG.The recognition rate of the network on small sample dataset and large sample dataset reaches 98.28%and 95.67%respectively.It provides a higher operation efficiency with reduced space complexity and time complexity. |
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| Keywords: | Spatial Transformer Network(STN) Heterogeneous Convolution(HetConv)filter AlexNet model Convolutional Neural Network(CNN) identification of liver cirrhosis |
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