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An Appraisal of Nodule Diagnosis for Lung Cancer in CT Images.

As "the second eyes" of radiologists, computer-aided diagnosis systems play a significant role in nodule detection and diagnosis for lung cancer. In this paper, we aim to provide a systematic survey of state-of-the-art techniques (both traditional techniques and deep learning techniques) for nodule...

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Published in:Journal of Medical Systems Vol. 43; no. 7
Main Authors: Zhang, Guobin, Yang, Zhiyong, Gong, Li, Jiang, Shan, Wang, Lu, Cao, Xi, Wei, Lin, Zhang, Hongyun, Liu, Ziqi
Format: diagnostic images equations & formulas review tables/charts Journal Article
Published: Springer Nature Jul2019
Online Access:View this record in EBSCOhost
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      dt: Jul2019
      vid: 43
      iid: 7
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10916-019-1327-0
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        atl: An Appraisal of Nodule Diagnosis for Lung Cancer in CT Images.
      aug:
        au:
          Zhang, Guobin
          Yang, Zhiyong
          Gong, Li
          Jiang, Shan
          Wang, Lu
          Cao, Xi
          Wei, Lin
          Zhang, Hongyun
          Liu, Ziqi
        affil: School of Mechanical Engineering, Tianjin University, 300350, Tianjin, China
      sug:
        subj:
          Lung Neoplasms Diagnosis
          Tomography, X-Ray Computed Methods
          Image Processing, Computer Assisted
          Neoplasms Pathology
          Neoplasm Staging
          Signal Processing, Computer Assisted
          Imaging, Three-Dimensional
          Neural Networks (Computer)
          Digital Imaging
      ab: As "the second eyes" of radiologists, computer-aided diagnosis systems play a significant role in nodule detection and diagnosis for lung cancer. In this paper, we aim to provide a systematic survey of state-of-the-art techniques (both traditional techniques and deep learning techniques) for nodule diagnosis from computed tomography images. This review first introduces the current progress and the popular structure used for nodule diagnosis. In particular, we provide a detailed overview of the five major stages in the computer-aided diagnosis systems: data acquisition, nodule segmentation, feature extraction, feature selection and nodule classification. Second, we provide a detailed report of the selected works and make a comprehensive comparison between selected works. The selected papers are from the IEEE Xplore, Science Direct, PubMed, and Web of Science databases up to December 2018. Third, we discuss and summarize the better techniques used in nodule diagnosis and indicate the existing future challenges in this field, such as improving the area under the receiver operating characteristic curve and accuracy, developing new deep learning-based diagnosis techniques, building efficient feature sets (fusing traditional features and deep features), developing high-quality labeled databases with malignant and benign nodules and promoting the cooperation between medical organizations and academic institutions.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        equations & formulas
        review
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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