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Lung Nodule Detection in CT Images Using a Raw Patch-Based Convolutional Neural Network.

Remarkable progress has been made in image classification and segmentation, due to the recent study of deep convolutional neural networks (CNNs). To solve the similar problem of diagnostic lung nodule detection in low-dose computed tomography (CT) scans, we propose a new Computer-Aided Detection (CA...

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Published in:Journal of Digital Imaging Vol. 32; no. 6; pp. 971 - 980
Main Authors: Wang, Qin, Shen, Fengyi, Shen, Linyao, Huang, Jia, Sheng, Weiguang
Format: diagnostic images equations & formulas research tables/charts Journal Article
Published: Springer Nature Dec2019
Online Access:View this record in EBSCOhost
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      dt: Dec2019
      vid: 32
      iid: 6
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10278-019-00221-3
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      tig:
        atl: Lung Nodule Detection in CT Images Using a Raw Patch-Based Convolutional Neural Network.
      aug:
        au:
          Wang, Qin
          Shen, Fengyi
          Shen, Linyao
          Huang, Jia
          Sheng, Weiguang
        affil: Shanghai Jiao Tong University, 201100, Shanghai, China
      sug:
        subj:
          Lung Neoplasms Radiography
          Tomography, X-Ray Computed Methods
          Neural Networks (Computer)
          Image Processing, Computer Assisted
          Human
          Sensitivity and Specificity
          False Positive Results
          Deep Learning
          Computer-Aided Design
      ab: Remarkable progress has been made in image classification and segmentation, due to the recent study of deep convolutional neural networks (CNNs). To solve the similar problem of diagnostic lung nodule detection in low-dose computed tomography (CT) scans, we propose a new Computer-Aided Detection (CAD) system using CNNs and CT image segmentation techniques. Unlike former studies focusing on the classification of malignant nodule types or relying on prior image processing, in this work, we put raw CT image patches directly in CNNs to reduce the complexity of the system. Specifically, we split each CT image into several patches, which are divided into 6 types consisting of 3 nodule types and 3 non-nodule types. We compare the performance of ResNet with different CNNs architectures on CT images from a publicly available dataset named the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). Results show that our best model reaches a high detection sensitivity of 92.8% with 8 false positives per scan (FPs/scan). Compared with related work, our work obtains a state-of-the-art effect.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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