WCF-MobileNetV3：Lightweight COVID-19 CXR Image Recognition Network

doi:10.3778/j.issn.1002-8331.2205-0558

Abstract

Abstract: To identify COVID-19 chest X-Ray（CXR） images accurately and quickly, a lightweight model WCF-MobileNetV3 based on weighted channel filter（WCF） is proposed. The model uses MobileNetV3-small as the backbone network. Aiming at the problem that the difference between CXR image samples is small and it is difficult to extract distinguishing features, the WCF module is proposed. First, it extracts the weights of the high-dimensional and low-dimensional channel features from the input feature map. And then, it generates the high-dimensional and low-dimensional feature channel masks by weighted random sampling and fuses the weights. Next, it uses the masks to filter the fused weights. Finally, it assigns the weights to the input feature map to achieve channel-wise feature enhancement. The experimental results on the Chest-X-Ray Image dataset and the COVID-19 Chest X-Ray Image Repository dataset show that the accuracy, precision, and sensitivity of WCF-MobileNeteV3 for COVID-19 recognition are 97.93%, 98.64%, and 97.19%, respectively. Compared with other COVID-19 identification algorithms, WCF-MobileNetV3 can identify COVID-19 CXR images accurately and efficiently with better recognition performance.

Key words: COVID-19, chest X-ray image, convolutional neural network, channel filter

摘要： 为了对新型冠状病毒引发的肺炎胸部X光（chest X-Ray，CXR）图像进行准确且快速的识别，提出了一种基于加权通道筛选（weighted channel filter，WCF）的轻量级模型WCF-MobileNetV3。将轻量级的MobileNetV3-small作为主干网络，并针对CXR图像样本类间差异小、难以提取区分性特征的问题，提出了WCF模块。提取输入特征图的高维与低维通道特征权重；采取加权随机抽样的方式生成高维与低维特征通道掩膜，将高维、低维的权重融合，并利用掩膜对融合后的权重进行通道筛选；将权重赋给输入特征图，实现通道特征增强。在Chest-X-Ray Image与COVID-19 Chest X-Ray Image Repository数据集上进行了实验，结果表明：WCF-MobileNetV3对新冠肺炎CXR图像识别的准确率、精确率、灵敏度分别为97.93%、98.64%、97.19%。与其他新冠肺炎识别算法相比，WCF-MobileNetV3能够准确且高效地识别新冠肺炎CXR图像，具有更好的识别性能。

关键词: 新冠肺炎, CXR图像, 卷积神经网络, 通道筛选

PENG Xinrui, PAN Qing, TIAN Nili. WCF-MobileNetV3：Lightweight COVID-19 CXR Image Recognition Network[J]. Computer Engineering and Applications, 2023, 59(14): 224-231.

彭心睿, 潘晴, 田妮莉. WCF-MobileNetV3：轻量型新冠肺炎CXR图像识别网络[J]. 计算机工程与应用, 2023, 59(14): 224-231.

References

[1] 高艳，戎冬冬，安彦虹，等.新型冠状病毒肺炎的X线及CT表现[J].CT理论与应用研究，2020，29（2）：147-154.
GAO Y，RONG D D，AN Y H，et al.X-ray and CT features of novel coronavirus pneumonia[J].CT Theory and Applications，2020，29（2）：147-154.
[2] LOEY M，SMARANDACHE F，KHALIFA N E M.Within the lack of chest COVID-19 X-ray dataset：a novel detection model based on GAN and deep transfer learning[J].Symmetry（Basel），2020，12（4）：651.
[3] APOSTOLOPOULOS I D，MPESIANA T A.Covid-19：automatic detection from X-ray images utilizing transfer learning with convolutional neural networks[J].Physical and Engineering Sciences in Medicine，2020，43（2）：635-640.
[4] OZTURK T，TALO M，TILDIRIM E A，et al.Automated detection of COVID-19 cases using deep neural networks with X-ray images[J].Computers in Biology and Medicine，2020，121：103792.
[5] TORAMAN S，ALAKUS T B，TURKOGLU I.Convolutional capsnet：a novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks[J].Chaos，Solitons and Fractals，2020，140：110122.
[6] OH Y J，PARK S J，YE J C.Deep learning COVID-19 features on CXR using limited training data sets[J].IEEE Transactions on Medical Imaging，2020，39（8）：2699-2700.
[7] MARQUES G，AGARWAL D，DIEZ ID.Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network[J].Applied Soft Computing，2020，96：106691.
[8] AGRAWAL T，CHOUDHARY P.FocusCovid：automated COVID-19 detection using deep learning with chest X-ray images[J].Evolving Systems，2021：1-15.
[9] KASSANIA S H，KASSANIB P H，WESOLOWSKIC M J，et al.Automatic detection of coronavirus disease（COVID-19） in X-ray and CT images：a machine learning based approach[J].Biocybernetics and Biomedical Engineering，2021，41（3）：867-879.
[10] DAS A.Adaptive UNet-based lung segmentation and ensemble learning with CNN-based deep features for automated COVID-19 diagnosis[J].Multimedia Tools and Applications，2022，81（4）：5407-5441.
[11] SHAH P M，ULLAH F，SHAH D，et al.Deep GRU-CNN model for COVID-19 detection from chest X-Rays data[J].IEEE Access，2022，10：35094-35105.
[12] BHOWAL P，SEN S，YOON J H，et al.Choquet integral and coalition game-based ensemble of deep learning models for COVID-19 screening from chest X-Ray images[J].IEEE Journal of Biomedical and Health Informatics，2021，25（12）：4328-4339.
[13] ZHAO H M，FANG Z Y，REN J C，et al.SC2Net：a novel segmentation-based classification network for detection of COVID-19 in chest X-Ray images[J].IEEE Journal of Biomedical and Health Informatics，2022，26（8）：4032-4043.
[14] XU X L，TIAN H，ZHANG X Y，et al.DisCOV：distributed COVID-19 detection on X-Ray images with edge-cloud collaboration[J].IEEE Transactions on Services Computing，2022，15（3）：1206-1219.
[15] MONDAL A K.COVID-19 prognosis using limited chest X-ray images[J].Applied Soft Computing，2022，122：108867.
[16] FU J L，ZHENG H L，MEI T.Look closer to see better：recurrent attention convolutional neural network for fine-grained image recognition[C]//Proceedings of the 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition，2017：4476-4484.
[17] HOU S H，WANG Z L.Weighted channel dropout for regularization of deep convolutional neural network[C]//Proceedings of the 9th AAAI Symposium on Educational Advances in Artificial Intelligence，2019：8425-8432.
[18] HU J，SHEN L，ALBANIE S，et al.Squeeze-and-excitation networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2020，42（8）：2011-2023.
[19] HOWARD A，SANDLER M，CHU G，et al.Searching for MobileNetV3[C]//Proceedings of IEEE/CVF International Conference on Computer Vision，2019：1314-1324.
[20] EFRAIMIDIS P S，SPIRAKIS P G.Weighted random sampling with a reservoir[J].Information Processing Letters，2006，97（5）：181-185.
[21] MOONEY P.Chest X-ray images （pneumonia）[EB/OL].（2018）[2022-05-28].https：//www.kaggle.com/paultimothymooney/chest-xray-pneumonia.
[22] HAGHANIFAR A，MAJDABADI M M，KO S B.COVID-CXNet：detecting COVID-19 in frontal chest X-ray images using deep learning[EB/OL].（2020-07-29）[2022-05-28].https：//arxiv.org/abs/2006.13807.
[23] IANDOLA F N，HAN S，MOSKEWICZ M W，et al.SqueezeNet：AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size[EB/OL].（2016-11-04）[2022-05-28].https：//arxiv.org/abs/1602.07360.
[24] HAN K，WANG Y H，TIAN Q，et al.GhostNet：more features from cheap operations[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition，2020：1577-1586.
[25] MA N N，ZHANG X Y，ZHENG H T，et al.ShuffleNetV2：practical guidelines for efficient CNN architecture design[C]//Proceedings of European Conference on Computer Vision，2018：122-138.
[26] SIMONYAN K，ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[EB/OL].（2015-04-10）[2022-05-28].https：//arxiv.org/abs/1409.1556.
[27] HE K M，ZHANG X Y，REN S Q，et al.Deep residual learning for image recognition[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition，2016：770-778.
[28] SZEGEDY C，LIU W，JIA Y Q，et al.Going deeper with convolutions[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition，2015：1-9.
[29] TAN M X，LE Q V.EfficientNet：rethinking model scaling for convolutional neural networks[C]//Proceedings of International Conference on Machine Learning，2019.
[30] RAHMAN T，CHOWDHURY M，KHANDAKAR A.COVID-19 radiography database[EB/OL].（2022-03）[2022-07-13].https：//www.kaggle.com/datasets/amanullahasraf/covid19-pneumonia-normal-chest-xraypa-dataset.
[31] SELVARAJU R R，COGSWELL M，DAS A，et al.Grad-CAM：visual explanations from deep networks via gradient-based localization[J].International Journal of Computer Vision，2020，128（2）：336-359.