首页|基于联邦学习与改进CBAM-ResNet18的脑肿瘤分类

基于联邦学习与改进CBAM-ResNet18的脑肿瘤分类

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针对联邦学习框架下,基于卷积注意力模块的多客户端脑肿瘤分类方法对于MRI图像中肿瘤区域细节提取能力不足、通道注意力与空间注意力相互干扰的问题,以及针对多点医疗肿瘤数据分类准确性低的问题,提出了一种融合联邦学习框架和改进的CBAM-ResNet18 网络的脑肿瘤分类方法.利用联邦学习特性联合多点脑肿瘤数据,采用Leaky ReLU激活函数代替ReLU激活函数以减轻神经元死亡,将卷积注意力模块中的通道注意力模块由先降维再升维改成先升维再降维,充分提高网络对图像细节的提取能力,将卷积注意力模块中的通道注意力模块与空间注意力模块由级联结构改为并联结构,使得网络的特征提取能力不会受到二者先后顺序的影响.通过在Kaggle公开的脑肿瘤MRI数据集上的进行实验,该方法的准确率、精准度、召回率与F1 值分别为 97.78%、97.68%、97.61%与 97.63%,比基准模型分别高 6.54%、4.78%、6.80%、7.00%.实验结果证明,该方法不仅能够打破数据孤岛,实现多点数据融合,而且比多数现有主流模型的性能更好.
Brain Tumor Classification Based on Federated Learning and Improved CBAM-ResNet18
The multi-client brain tumor classification method based on the convolutional block attention module has inadequate extraction of tumor region details from MRI images,and channel attention and spatial attention interfere with each other under the federated learning framework.In addition,the accuracy in classifying medical tumor data from multiple points is low.To address these problems,this study proposes a brain tumor classification method that amalgamates the federated learning framework with an enhanced CBAM-ResNet18 network.The method leverages the federated learning characteristic to collaboratively work with brain tumor data from multiple sources.It replaces the ReLU activation function with Leaky ReLU to mitigate issues of neuron death.The channel attention module within the convolutional block attention module is modified from a dimension reduction followed by a dimension increment approach to a dimension increment followed by a dimension reduction approach.This change significantly enhances the network's ability to extract image details.Furthermore,the architecture of the channel attention module and spatial attention module in the convolutional block attention module has been shifted from a cascade structure to a parallel structure,ensuring that the network's feature extraction capability remains unaffected by the order of processing.A publicly available brain tumor MRI dataset from Kaggle is used in the study.The results demonstrate that FL-CBAM-DIPC-ResNet has a remarkable performance.It achieves impressive accuracy,precision,recall,and F1 score of 97.78%,97.68%,97.61%,and 97.63%,respectively.These values of accuracy,precision,recall,and F1 score are 6.54%,4.78%,6.80%,and 7.00%higher than those of the baseline model.These experimental findings validate that the proposed method not only overcomes data islands and enables data fusion from multiple sources but also outperforms the majority of existing mainstream models in terms of performance.

brain tumor classificationfederated learningconvolutional block attention module(CBAM)residual network(ResNet)data island

吴波、史东辉、吕东来、胡涛

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安徽建筑大学电子与信息工程学院,合肥 230601

质谱关键技术研发与临床应用安徽省联合共建学科重点实验室,合肥 230601

中国人民解放军联勤保障部队第901医院肿瘤科,合肥 230031

脑肿瘤分类 联邦学习 卷积注意力模块 残差网络 数据孤岛

国家自然科学基金青年基金安徽省科研编制计划安徽省重点研发计划质谱关键技术研发与临床应用安徽省联合共建学科重点实验室开放基金

620010042022AH050224202004a070200502023ZPLH07

2024

10.15888/j.cnki.csa.009469

计算机系统应用
中国科学院软件研究所

计算机系统应用

CSTPCD
影响因子：0.449
ISSN：1003-3254
年,卷(期)：2024.33(4)
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