2016
DOI: 10.1016/j.cmpb.2015.10.006
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Abstract: This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images.Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition, storage and, mainly, processing and analysis of the biomedical data. Also, this work identifies the progress made, so far, evaluates the challenges to be overcome and provides an analysis of future prospects. As far as the a… Show more

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Cited by 197 publications
(87 citation statements)
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“…Image-based techniques for analyzing lesions are normally performed with detection [7,8], segmentation [9][10][11][12], hand-crafted feature engineering [13,14], and category labelling [15][16][17][18]. Zinovev et al [19] adopted a belief decision tree approach to predict nodule semantic attributes.…”
Section: Introductionmentioning
confidence: 99%
“…Image-based techniques for analyzing lesions are normally performed with detection [7,8], segmentation [9][10][11][12], hand-crafted feature engineering [13,14], and category labelling [15][16][17][18]. Zinovev et al [19] adopted a belief decision tree approach to predict nodule semantic attributes.…”
Section: Introductionmentioning
confidence: 99%
“…The main reason for lung cancer is the formation of cancerous nodules in lung lobes or lung periphery. Nodules can be defined as lung tissue abnormalities having a roughly spherical structure and diameter of up to 30 mm . They can be classified into the following categories: well‐circumscribed, juxta‐vascular, juxta‐pleural, and pleural tail.…”
Section: Introductionmentioning
confidence: 99%
“…Image acquisition can be defined as a process of acquiring medical images from imaging modalities . Many common methods are available for lung imaging.…”
Section: Introductionmentioning
confidence: 99%
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“…The recent remarkable and significant progress in deep learning for pulmonary nodules achieved in both academia and the industry has demonstrated that deep learning techniques seem to be promising alternative decision support schemes to effectively tackle the central issues in pulmonary nodules diagnosing, including feature extraction, nodule detection, false-positive reduction (3,5,6,(11)(12)(13). Deep learning aided decision support for pulmonary nodules diagnosing: a review classification between benign and malignant in terms of the number of publications with respect to the time.…”
Section: Introductionmentioning
confidence: 99%