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Laser & Optoelectronics Progress, Volume. 58, Issue 16, 1600005(2021)

Application of Deep Learning Methods in Diagnosis of Lung Nodules

Bin Cao1, Feng Yang1, and Jingang Ma2、*
Author Affiliations
  • 1Shandong Provincial Hospital of Traditional Chinese Medicine, Jinan, Shandong 250000, China
  • 2School of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250355, China
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    Figures&Tables (6)
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    Figures & Tables(6)
    Schematic diagram of segmentation of lung nodules by U-Net

    Fig. 1. Schematic diagram of segmentation of lung nodules by U-Net

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    Comparison of 2D-CNN and 3D-CNN structures

    Fig. 2. Comparison of 2D-CNN and 3D-CNN structures

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    Schematic diagram of Faster R-CNN lung nodule detection

    Fig. 3. Schematic diagram of Faster R-CNN lung nodule detection

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    Specific structure of DBN classifier

    Fig. 4. Specific structure of DBN classifier

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    • Table 1. Data set of lung nodule diagnosis field

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      Table 1. Data set of lung nodule diagnosis field

      DatasetImage formatNumber of casesNumber of CT scansNodule tagNote
      LIDC-IDRI[7]DICOM10101018*Open
      LUNA16[8]mhd888888*Not open
      DSB[10]DICOM21012 101--Not open
      NLST[11]DICOM--3 410*Open
      DLCST[12]DICOM82317*Open
      LUNGx[13]DICOM--70*Open
      ANODEO9[14]DICOM--55*Open
      AliTianchi[15]mhd16002 000*Not open

    • Table 2. Comparison of results of pulmonary nodule detection models in selected literatures

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      Table 2. Comparison of results of pulmonary nodule detection models in selected literatures

      ReferenceTimeDatasetCT numberFP per scanMethodSensitivity /%Note
      Miao et al.[19]2018LIDC-IDRI8881.0 or 4.02D87.32D FCN
      Chen et al.[20]2017DLCST61262D76.5CNN
      Tong et al.[22]2018LIDC-IDRI8881.0 or 202D96.7 or 98.3U-Net+ResNet
      Ruan et al.[23]2020LUNA168881.0 or 4.02D90.1LSTM+ U-Net
      Setio et al. [24]2017LUNA168881.0 or 4.02D85.4 or 90.1CNN
      Dou et al.[25]2017LIDC-IDRI888219.13D97.1FCN+ResNet
      Liu et al.[26]2017LIDC-IDRI888--3D95.83D-DenseNet+U-net
      Cao et al.[29]2020LIDC-IDRI8881.0 or 4.03D85.4CNN
      Hou et al. [30]2020LUNA16888--3D92.33D U-Net+CRF
      Dehmeshki et al. [31]2008LIDC-IDRI25--3D93.4U-Net+ResNet
      Xie et al. [33]2019LUNA168880.125 or 0.253D73.4 or 74.4CNN
      Yuan et al. [35]2018LIDC-IDRI888--3D96.7 or 98.3CNN
      Xie et al. [38]2019LUNA16888--2D+3D86.42Faster R-CNN
      Ding et al. [39]2017LUNA168881.0 or 4.02D+3D92.2 or 94.4Faster R-CNN+CNN
      Zheng et al. [40]2020LIDC-IDRI8881.0 or 2.02D+3D92.67 or 94.19U-Net+Faster R-CNN
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    Bin Cao, Feng Yang, Jingang Ma. Application of Deep Learning Methods in Diagnosis of Lung Nodules[J]. Laser & Optoelectronics Progress, 2021, 58(16): 1600005

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    Paper Information

    Category: Reviews

    Received: Nov. 9, 2020

    Accepted: Dec. 17, 2020

    Published Online: Aug. 19, 2021

    The Author Email: Ma Jingang (ma_jingang@126.com)

    DOI:10.3788/LOP202158.1600005

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology