基于改进EfficientNet的阿尔兹海默症图像分类

doi:10.3969/j.issn.1006-2475.2023.06.010

计算机与现代化 ›› 2023, Vol. 0 ›› Issue (06): 56-61.doi: 10.3969/j.issn.1006-2475.2023.06.010

基于改进EfficientNet的阿尔兹海默症图像分类

朱剑波¹, 葛明锋², 董文飞²

1.山东中医药大学智能与信息工程学院,山东济南 250355;
2.中国科学院苏州生物医学工程技术研究所医学检验技术研究室,江苏苏州 215163

收稿日期:2022-07-02 修回日期:2022-07-19 出版日期:2023-06-28 发布日期:2023-06-28
通讯作者: 葛明锋（1987—）,男,江苏南通人,副研究员,硕士生导师,博士,研究方向：高光谱显微成像及图像处理,E-mail： gemf@sibet.ac.cn。
作者简介:朱剑波（1998—）,男,江苏南京人,硕士研究生,研究方向：生物医学信息检测,医疗设备应用技术,E-mail： 1152067715@qq.com; 董文飞（1975—）,男,吉林长春人,研究员,博士生导师,博士,研究方向：纳米生物医学工程及其在药物递送、生物光子成像中心应用,E-mail： wenfeidong@sibet.ac.cn。
基金资助:
国家重点研发计划项目（2021YFB3600117）; 中科院仪器设备研制项目（YJKYYQ20200038）; 江苏省重点研发计划项目（BE2019683）

Alzheimer’s Disease Image Classification Based on Improved EfficientNet

ZHU Jian-bo¹, GE Ming-feng², DONG Wen-fei²

1. School of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan 250355, China;
2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China

Received:2022-07-02 Revised:2022-07-19 Online:2023-06-28 Published:2023-06-28

摘要/Abstract

摘要： 为提升卷积神经网络用于阿尔兹海默症MRI图像分类的效果,提出一种融合自适应注意力机制和数据增强技术的卷积神经网络FAMENET。通过引入数据增强技术和Focal Loss损失函数缓解数据不平衡现象;重构优化主干网络 EfficientNet,在保持精度的情况下减少模型参数量和网络的计算量;引入自适应注意力机制,解决输入图片进行特征提取下采样过程导致的信息丢失问题。在公开数据集进行大量对比实验,FAMENET的分类准确率达到79.95%,AUC值达到82.54%,设计的消融实验也充分证明了所提出的各个模块和网络的有效性。

关键词: 卷积神经网络, 阿尔兹海默症, 自适应注意力机制, 数据增强, 医学图像分类

Abstract: To improve the effectiveness of the convolutional neural network for Alzheimer’s disease MRI image classification, a convolutional neural network FAMENET is proposed, which integrates an adaptive attention mechanism and data enhancement technique to alleviate data imbalance by introducing a data augmentation technique and Focal Loss loss function. The network is reconfigured to reduce the number of model parameters and the computational effort of the network while maintaining accuracy. The adaptive attention mechanism is introduced to solve the information loss problem caused by the downsampling of input images for feature extraction. In a large number of comparative experiments on public datasets, the classification accuracy of FAMENET reaches 79.95% and the AUC value reaches 82.54%. The designed ablation experiments also fully demonstrate the effectiveness of the proposed modules and networks.

Key words: convolutional neural network, Alzheimer’s Disease, convolutional block attention module, data augmentation, medical image classification

中图分类号:

TP183

朱剑波, 葛明锋, 董文飞. 基于改进EfficientNet的阿尔兹海默症图像分类[J]. 计算机与现代化, 2023, 0(06): 56-61.

ZHU Jian-bo, GE Ming-feng, DONG Wen-fei. Alzheimer’s Disease Image Classification Based on Improved EfficientNet[J]. Computer and Modernization, 2023, 0(06): 56-61.

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基于改进EfficientNet的阿尔兹海默症图像分类

Alzheimer’s Disease Image Classification Based on Improved EfficientNet

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