The current application status of deep learning in Chest X-ray screening for lung diseases

doi:10.19982/j.issn.1000-6621.20240245

Abstract

Abstract:

There are various types of lung diseases with serious harm, and early detection of those diseases is crucial to improve the survival rate of patients. In recent years, deep learning technology has made a breakthrough progress in analysis of medical images, which providing new possibilities for early screening of lung diseases. The authors reviewed relevant studies in the past five years, focusing on the applications of Convolutional Neural Networks, Transformer models, and their hybrid architectures in chest X-ray (CXR) image analysis. Additionally, the potential of multi-model ensemble learning strategies and attention mechanisms in improving the diagnostic accuracy of lung diseases was also analyzed. This comprehensive review aims to systematically review and analyze the current application, challenges, and future directions of using deep learning technologies in chest X-ray screening for lung disease detection.

Key words: Fluoroscopy, Neural networks (computer), Lung diseases

CLC Number:

TP391.41
R81

Li Junliang, Liu Xin, Lin Zhiyuan, Long Xianrong, Jiang Zhihang, Huo Yingyu. The current application status of deep learning in Chest X-ray screening for lung diseases[J]. Chinese Journal of Antituberculosis, 2024, 46(12): 1548-1559. doi: 10.19982/j.issn.1000-6621.20240245

Figures/Tables 9

文献	思想	数据	评估	优缺点
[31]	含有5个卷积层、池化层及2个密集层的模型	新型冠状病毒感染:3616;肺炎:4273;无肺部病变:10192;肺结核:3500	98.63%的准确率和98.35%的召回率	模型结构简单且效果优秀,但鲁棒性差,层数较浅
[32]	使用6种不同的卷积核并列进行特征提取,以极限学习机作为分类器	ChestXray14数据集;病毒性肺炎:1493;肺结核:1036	90.92%的准确率和96.93%的曲线下面积	模型捕获多尺度特征强,轻量化程度高
[33]	VGG19+4个密集层	新型冠状病毒感染:3615;肺炎:11726;密度增高病灶:6012;肺癌:20000;无肺部病变:37247;肺结核:1400	96.48%的准确率、93.75%的召回率、99.82%的曲线下面积	使用较小的卷积块作特征提取,通过增加网络深度或加入注意力的方式丰富多层次的特征信息
[34]	VGG16+3个3×3的卷积块和1个全局平均池化层	无肺部病变:13672;肿块:5603;新型冠状病毒感染:15660;结节:6201;胸腔积液:13501;肺炎:9878;纤维化:3357;肺结核:3184;密度增高病灶:7179;气胸:6870	98.89%的准确率和99.87%的特异度	使用较小的卷积块作特征提取,通过增加网络深度或加入注意力的方式丰富多层次的特征信息
[41]	VGG16中加入了水平和垂直方向的坐标注意机制	深圳数据集	92.73%的准确率和97.71%的曲线下面积	使用较小的卷积块作特征提取,通过增加网络深度或加入注意力的方式丰富多层次的特征信息
[36]	以ResNet50的4个具有残差结构的卷积块提取特征	肺炎胸部X线图像数据集;新型冠状病毒感染影像数据库	98.35%的准确率	以快捷连接的方式使模型更深、更复杂
[42]	DenseNet+由卷积构成的特征选择器+具有空间和通道编码能力的特征集成器	ChestX-ray14数据集;CheXpert数据集	84.1%和92.1%的准确率	从多个通道分别获取丰富的特征信息,更具有鲁棒性
[38]	VGG16、Inception v3和ResNet 50网络的集成	COVID-ChestXray数据集	特异度为98.54%,精确度为93.60%	充分利用每个网络的优势,但模型的复杂度和训练难度更大
[39]	MobileNetV2、InceptionV3和VGG19的集成	密度增高病灶:6914;无肺部病变:11192;新型冠状病毒感染:11582;肺炎:10036;肺结核:2984	四分类和五分类的准确度分别为87.12%和91.71%	充分利用每个网络的优势,但模型的复杂度和训练难度更大

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文献编号	数据集	图像种类(病种)	图片像素	数量
[18]	Shenzhen	包含336张肺结核和326张无肺部病变的胸部X线摄片图像	3000×3000	662
[18]	Montgomery	包含80张无肺部病变和58张肺结核的胸部X线摄片图像	4892×4020	138
[19]	ChestXray14	包含14种肺部病变图像(肺不张、实变、浸润、气胸、水肿、肺气肿、纤维化、胸腔积液、肺炎、胸膜增厚、心脏肥大、结节、肿块和疝气)	1024×1024	112120
[20]	CheXpert	包含14种类别图像(无肺部病变、纵隔扩大、心脏肥大、密度增高病灶、肺部病变、水肿、变实、肺炎、肺不张、气胸、胸腔积液、胸膜其他病变、骨折、辅助设备)	-	224316
[21]	CoronaHack	包括4种类别(无肺部病变、新型冠状病毒感染、病毒性肺炎、细菌性肺炎)	384×127、 1214×937、 4248×3480	5933
[22]	COVID-ChestXray-15k	包括3种图像(无肺部病变、肺炎、新型冠状病毒感染)	1024×1024	15000
[23]	COVQU	包含新型冠状病毒感染、密度增高病灶、病毒性肺炎、无肺部病变	1024×1024	21165
[17]	TBX11K	包括无肺部病变、异常但不属于肺结核图像	512×512	11200
[24]	JSRT	提供了100张恶性结节、54张良性结节和93张无结节的胸部X线摄片图像	2048×2048	247

方法	文献	预处理思想	数据	评估
数字图像处理
	[25]	自适应直方图均衡化处理	病毒性肺炎:1495;细菌性肺炎:2779;无肺部病变:1583	准确率为98.32%
	[26]	基于K-Lerch超越函数模型的图像增强处理	无肺部病变:417+277;肺炎:250+196;新型冠状病毒感染:237+337^a	X线摄片和CT图像准确率分别为98.60%和98.80%
预特征提取方法
	[27]	主动形状模型进行肺野分割+主成分分析对肋骨建模	肺结节:548;无肺部病变:177	平均曲线下面积为0.815
	[28]	以U型网络模型作为骨干模型的ResNet 18和EfficientNet B0的集成学习	新型冠状病毒感染:6032;无肺部病变:3136	准确率为98.20%,曲线下面积为0.998
	[29]	使用四路卷积编码的改进U型网络模型对胸部X线摄片图像进行肺部区域分割	ChestXray14	准确率为84.2%
	[30]	使用U型网络提取多尺度特征图,基于特征金字塔融合多尺度特征图,实现肺部轮廓分割	无肺部病变:8062;肺炎:5501;新型冠状病毒感染:562	准确率为91.3%

文献	思想	数据	评估	优缺点
[46]	Vision Transformer	无肺部病变:10314;新型冠状病毒感染:10819;肺炎:10702	98%的准确率和99%的曲线下面积	模型简单,易于实现,但是没有考虑局部信息的重要性
[47]	Vision Transformer+输入图像的3组2D卷积特征	无肺部病变:29477;肺炎:5852;肺结核:700;新型冠状病毒感染:20305	98.29%的平均准确率和98.95%的平均精确度	充分利用卷积的局部感受野和平移不变性,以及Transformer的全局注意力机制,对图像进行全局的语义建模和特征交互;Transformer的长程注意力机制容易导致图像块内部结构信息被破坏
[48]	Vision Transformer+2组不同卷积核大小的深度卷积	无肺部病变:10083+6893;新型冠状病毒感染:3466+7593;病毒性肺炎:1342+2618^a	X线和CT图像的准确率分别达到97.25%和98.36%	同上
[54]	Swin Transformer+2层密集层和2层Drop out层	新型冠状病毒感染:3616;密度增高病灶:6012;病毒性肺炎:1345;无肺部病变:10192	98%的召回率和96%的准确率	同上
[50]	结合图像的多头注意力与标签注意力的双路径解码器	ChestXray14数据集	83.1%的曲线下面积	特征图和标签向量进行信息交互,但是模型复杂、训练难度较高
[51]	Transformer图像编码+细节校正路径从X和Y像素方向的梯度信息+MagNet根据训练集分布调整测试集分布	Synthesis-covid-cxr数据集	95.23%的准确率	能够根据数据特点自适应地调节测试数据分布,聚焦更重要的内容
[49]	pyramid vision Transformer变体模型	ChestXray14数据集	83.0%的曲线下面积	位置编码的方式取代位置嵌入,减少注意力的计算量

文献	思想	数据	评估	优缺点
[55]	EfficientNet+Vision Transformer	蒙哥马利数据集、深圳数据集、新型冠状病毒感染影像数据库	97.51%的准确率和98%的召回率	优点:能够捕获多尺度上下文信息
[56]	L和AB坐标+Inception V3+Transformer	新型冠状病毒感染影像数据库、COVID-ChestX-ray-15k数据集	分别获得96.66%和97.87%的准确率	优点:能够捕获多尺度上下文信息
[57]	DenseNet与MaxViT组合成双通道模型	COVID-QU-Ex数据集	95.61%的准确率和96%的召回率	优点:减少提取特征时信息的损失,增强有用的信息,抑制不重要的信息;缺点:模型参数量大
[58]	ResNet101+多级自注意力的Transformer	无肺部病变:10192;新型冠状病毒感染:3615;密度增高病灶:6023;病毒性肺炎:1345	96.54%的准确率	优点:减少提取特征时信息的损失,增强有用的信息,抑制不重要的信息;缺点:模型参数量大
[59]	ResNet18作特征提取,提取的特征作为单独patch+Vision Transformer	肺炎:11263;新型冠状病毒感染:11956;无肺部病变:10701	94.96%的准确率	优点:特征图不是裁切成多个patch,而是对整个特征图计算注意力,直接理解全图
[60]	VGG、Googlenet、Densenet集成学习+Vision Transformer	新型冠状病毒感染影像数据库、COVID-ChestX-ray-15k数据集	分别获得98.00%和97.4%的平均精确度	优点:可有效获得不同层次之间由低阶到高阶的互补信息;缺点:特征图在多个级别上密集级联,易导致高计算资源需求
[61]	DenseNet121、VGG16、Effi-cientNet V2集成学习+Vision Transformer	肺结核:57087;无肺部病变:18946	90.57%的准确度	优点:可有效获得不同层次之间由低阶到高阶的互补信息;缺点:特征图在多个级别上密集级联,易导致高计算资源需求