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Review

Automatic Pulmonary Nodule Detection Applying Deep Learning or Machine Learning Algorithms to the LIDC-IDRI Database: A Systematic Review

1
Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark
2
Department of Technology, Faculty of Health and Technology, University College Copenhagen, 2200 Copenhagen, Denmark
*
Author to whom correspondence should be addressed.
Diagnostics 2019, 9(1), 29; https://doi.org/10.3390/diagnostics9010029
Received: 20 December 2018 / Revised: 29 January 2019 / Accepted: 19 February 2019 / Published: 7 March 2019
(This article belongs to the Section Medical Imaging)
The aim of this study was to provide an overview of the literature available on machine learning (ML) algorithms applied to the Lung Image Database Consortium Image Collection (LIDC-IDRI) database as a tool for the optimization of detecting lung nodules in thoracic CT scans. This systematic review was compiled according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only original research articles concerning algorithms applied to the LIDC-IDRI database were included. The initial search yielded 1972 publications after removing duplicates, and 41 of these articles were included in this study. The articles were divided into two subcategories describing their overall architecture. The majority of feature-based algorithms achieved an accuracy >90% compared to the deep learning (DL) algorithms that achieved an accuracy in the range of 82.2%–97.6%. In conclusion, ML and DL algorithms are able to detect lung nodules with a high level of accuracy, sensitivity, and specificity using ML, when applied to an annotated archive of CT scans of the lung. However, there is no consensus on the method applied to determine the efficiency of ML algorithms. View Full-Text
Keywords: deep learning; machine learning; nodule detection deep learning; machine learning; nodule detection
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MDPI and ACS Style

Pehrson, L.M.; Nielsen, M.B.; Ammitzbøl Lauridsen, C. Automatic Pulmonary Nodule Detection Applying Deep Learning or Machine Learning Algorithms to the LIDC-IDRI Database: A Systematic Review. Diagnostics 2019, 9, 29. https://doi.org/10.3390/diagnostics9010029

AMA Style

Pehrson LM, Nielsen MB, Ammitzbøl Lauridsen C. Automatic Pulmonary Nodule Detection Applying Deep Learning or Machine Learning Algorithms to the LIDC-IDRI Database: A Systematic Review. Diagnostics. 2019; 9(1):29. https://doi.org/10.3390/diagnostics9010029

Chicago/Turabian Style

Pehrson, Lea M., Michael B. Nielsen, and Carsten Ammitzbøl Lauridsen. 2019. "Automatic Pulmonary Nodule Detection Applying Deep Learning or Machine Learning Algorithms to the LIDC-IDRI Database: A Systematic Review" Diagnostics 9, no. 1: 29. https://doi.org/10.3390/diagnostics9010029

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