A Convolutional Neural Network for Brain CT Image Classification Based on Residual Hybrid Attention Mechanism

QIAO Si-bo; PANG Shan-chen; WANG Min; ZHAI Xue; YU Shi-hang; DING Tong

doi:10.12263/DZXB.20200881

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A Convolutional Neural Network for Brain CT Image Classification Based on Residual Hybrid Attention Mechanism

更新时间：2025-12-08

- A Convolutional Neural Network for Brain CT Image Classification Based on Residual Hybrid Attention Mechanism
- Acta Electronica Sinica Vol. 49, Issue 5, Pages: 984-991(2021)
- 作者机构：
  
  1. 中国石油大学(华东)计算机科学与技术学院,山东,青岛,266580
  2. 中国石油大学(华东)控制科学与工程学院,山东,青岛,266580
  3. 天津工业大学机械工程学院,天津,300387
  4. 山东大学软件学院,山东,济南,250101
  5. 中国石油大学(华东)计算机科学与技术学院,山东,青岛,266580
  6. 中国石油大学(华东)控制科学与工程学院,山东,青岛,266580
  7. 天津工业大学机械工程学院,天津,300387
  8. 山东大学软件学院,山东,济南,250101
- 作者简介：
- 基金信息：
  
  Major Science and Technology Innovation Project of Shandong Province (No.2019TSLH0214);Taishan Industry Leading Talent Project (No.tscy20180416);National Natural Science Foundation of China (No.61873281, No.61972416)
- DOI：10.12263/DZXB.20200881
  CLC： TP399
- Published：2021
- 稿件说明：
移动端阅览
QIAO Si-bo, PANG Shan-chen, WANG Min, et al. A Convolutional Neural Network for Brain CT Image Classification Based on Residual Hybrid Attention Mechanism[J]. Acta Electronica Sinica, 2021, 49(5): 984-991.
DOI：

QIAO Si-bo, PANG Shan-chen, WANG Min, et al. A Convolutional Neural Network for Brain CT Image Classification Based on Residual Hybrid Attention Mechanism[J]. Acta Electronica Sinica, 2021, 49(5): 984-991. DOI： 10.12263/DZXB.20200881.

摘要

针对阿尔兹海默症、病变(如脑肿瘤)和健康老化的3类脑部CT图像分类问题

本文提出了一种改进的ResNet-10卷积神经网络模型.该模型在网络的残差映射结构中加入残差混合注意力模块

解决了原模型提取的特征分辨性弱的问题

精确捕捉了脑部组织在CT图像中的位置和内容信息;此外

本文设计了全局平均池化层

简化了模型的复杂度

并在其后引入Dropout机制

缓解了过拟合.在训练阶段

该模型建立了标签平滑交叉熵损失函数

使模型在样本数量有限的情况下仍有较强的泛化能力.系列实验证明了改进后的ResNet-10网络模型在分类脑部CT图像时达到97.47%的分类精度.

Abstract

To classify three types of brain CT (computerized tomography) images in Alzheimer's disease

lesion (e.g.

brain tumor) and healthy aging

an improved ResNet-10 convolutional neural network is proposed in this papers. A residual hybrid attention module is embedded in the residual identity mapping to capture the location and content information of brain tissue in brain CT images

solving the original model to extract weak distinguish features problems. In addition

to simplify the improved model and alleviate the overfitting

several techniques such as global average pooling and Dropout are used in the model. Moreover

to have strong generalization ability in the case of limited sample quantity

tag smoothing cross-entropy loss function is adopted to train the model. Experimental results show that the improved ResNet-10 achieves 97.47% accuracy in classifying brain CT images.

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