• Optics and Precision Engineering
  • Vol. 31, Issue 7, 1074 (2023)
Tao ZHOU1,3, Xinyu YE1,3,*, Huiling LU2, Yuncan LIU1,3, and Xiaoyu CHANG1,3
Author Affiliations
  • 1College of Computer Science and Engineering, North Minzu University, Yinchuan75002, China
  • 2College of Science, Ningxia Medical University, Yinchuan750003, China
  • 3Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan750021, China
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    DOI: 10.37188/OPE.20233107.1074 Cite this Article
    Tao ZHOU, Xinyu YE, Huiling LU, Yuncan LIU, Xiaoyu CHANG. Pneumonia aided diagnosis model based on dense dual-stream focused network[J]. Optics and Precision Engineering, 2023, 31(7): 1074 Copy Citation Text show less
    References

    [1] M EZHILAN, I SURESH, N NESAKUMAR. SARS-CoV, MERS-CoV and SARS-CoV-2: a diagnostic challenge. Measurement: Journal of the International Measurement Confederation, 168, 108335(2021).

    [2] Centers for Disease Control and Prevention. https://www.cdc.gov/pneumonia/prevention.html(2020).

    [3] M TOĞAÇAR, B ERGEN, Z CÖMERT. COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches. Computers in Biology and Medicine, 121, 103805(2020).

    [4] 4周涛, 霍兵强, 陆惠玲, 等. 融合多尺度图像的密集神经网络肺部肿瘤识别算法[J]. 光学 精密工程, 2021, 29(7): 1695-1708. doi: 10.37188/OPE.20212907.1695ZHOUT, HUOB Q, LUH L, et al. Lung tumor image recognition algorithm with densenet fusion multi-scale images[J]. Opt. Precision Eng., 2021, 29(7): 1695-1708.(in Chinese). doi: 10.37188/OPE.20212907.1695

    [5] R JAIN. Pneumonia detection in chest X-ray images using convolutional neural networks and transfer learning. Measurement, 165, 108046(2020).

    [6] P RAJPURKAR, J IRVIN, K ZHU et al. CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv, 1711-05225(2017). https://arxiv.org/abs/1711.05225

    [7] E ÇALLl, E SOGANCIOGLU, B VAN GINNEKEN et al. Deep learning for chest X-ray analysis: a survey. Medical Image Analysis, 72, 102125(2021).

    [8] B Z CHEN. DualCheXNet: dual asymmetric feature learning for thoracic disease classification in chest X-rays. Biomedical Signal Processing and Control, 53, 101554(2019).

    [9] H LI, S S ZHUANG, D A LI et al. Benign and malignant classification of mammogram images based on deep learning. Biomedical Signal Processing and Control, 51, 347-354(2019).

    [10] B L CHEN, T S ZHAO, J H LIU et al. Multipath feature recalibration DenseNet for image classification. International Journal of Machine Learning and Cybernetics, 12, 651-660(2021).

    [11] 11陈筱, 朱向冰, 吴昌凡, 等. 基于迁移学习与特征融合的眼底图像分类[J]. 光学 精密工程, 2021, 29(2): 388-399. doi: 10.37188/OPE.20212902.0388CHENX, ZHUX B, WUCH F, et al. Research on fundus image classification based on transfer learning and feature fusion[J]. Opt. Precision Eng., 2021, 29(2): 388-399.(in Chinese). doi: 10.37188/OPE.20212902.0388

    [12] Y J LIU, P Y HAO, P ZHANG et al. Dense convolutional binary-tree networks for lung nodule classification. IEEE Access, 6, 49080-49088(2018).

    [13] K V PRIYA. A federated approach for detecting the chest diseases using DenseNet for multi-label classification. Complex & Intelligent Systems, 8, 3121-3129(2022).

    [14] M NASEER, K RANASINGHE, S KHAN et al. Intriguing properties of vision transformers. arXiv, 2105-10497(2021). https://arxiv.org/abs/2105.10497

    [15] 15景海钊, 史江林, 邱梦哲, 等. 基于密集残差块生成对抗网络的空间目标图像超分辨率重建[J]. 光学 精密工程, 2022, 30(17): 2155-2165. doi: 10.37188/OPE.20223017.2155JINGH ZH, SHIJ L, QIUM ZH, et al. Super-resolution reconstruction method for space target images based on dense residual block-based GAN[J]. Opt. Precision Eng., 2022, 30(17): 2155-2165.(in Chinese). doi: 10.37188/OPE.20223017.2155

    [16] D S KERMANY, K ZHANG, M GOLDBAUM. Labeled optical coherence tomography (OCT) and chest X-ray images for classification. Mendeley data(2018).

    [17] A GIEŁCZYK, A MARCINIAK, M TARCZEWSKA et al. Pre-processing methods in chest X-ray image classification. PLoS One, 17(2022).

    [18] I RADOSAVOVIC, R P KOSARAJU, R GIRSHICK et al. Designing network design spaces, 10425-10433(2020).

    [19] A HASSANI, S WALTON, J LI et al. Neighborhood Attention Transformer. arXiv, 2204-07143(2022). https://arxiv.org/abs/2204. 07143

    [20] M E ELARABY. A novel Gray-Scale spatial exploitation learning Net for COVID-19 by crawling Internet resources. Biomedical Signal Processing and Control, 73, 103441(2022).

    [21] M CHETOUI, M A AKHLOUFI. Explainable vision transformers and radiomics for COVID-19 detection in chest X-rays. Journal of Clinical Medicine, 11, 3013(2022).

    [22] K BALASUBRAMANIAN, N P ANANTHAMOORTHY, K RAMYA. An end-end deep learning framework for lung infection recognition using attention-based features and cross average pooling. International Journal for Multiscale Computational Engineering, 20, 67-82(2022).

    [23] I A KHAN. CoroNet: a deep neural network for detection and diagnosis of COVID-19 from chest X-ray images. Computer Methods and Programs in Biomedicine, 196, 105581(2020).

    [24] C OUCHICHA, O AMMOR, M MEKNASSI. CVDNet: a novel deep learning architecture for detection of coronavirus (Covid-19) from chest X-ray images. Chaos, Solitons, and Fractals, 140, 110245(2020).

    Tao ZHOU, Xinyu YE, Huiling LU, Yuncan LIU, Xiaoyu CHANG. Pneumonia aided diagnosis model based on dense dual-stream focused network[J]. Optics and Precision Engineering, 2023, 31(7): 1074
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