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An Appraisal of Nodule Diagnosis for Lung Cancer in CT Images

  • Image & Signal Processing
  • Published:
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Abstract

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.

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Acknowledgements

This study was funded by the National Natural Science Foundation of China (grant number 81871457), National Natural Science Foundation of China (grant number 51811530310), National Natural Science Foundation of China (grant number 51775368) and Science and Technology Planning Project of Guangdong Province, China (grant number 2017B020210004) and the Science and Technology Project of Tianjin (grant number 18YFZCSY01300).

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Authors and Affiliations

  1. School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China

    Guobin Zhang, Zhiyong Yang, Li Gong, Shan Jiang, Lu Wang, Xi Cao, Lin Wei, Hongyun Zhang & Ziqi Liu

  2. Centre for advanced Mechanisms and Robotics, Tianjin University, 135 Yaguan Road, Jinnan District, Tianjin, 300350, China

    Shan Jiang

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  1. Guobin Zhang
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Correspondence to Shan Jiang.

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Zhang, G., Yang, Z., Gong, L. et al. An Appraisal of Nodule Diagnosis for Lung Cancer in CT Images. J Med Syst 43, 181 (2019). https://doi.org/10.1007/s10916-019-1327-0

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