Feature-Extraction Methods for Lung-Nodule Detection: A Comparative Deep Learning Study

Brahim Ait Skourt, Nikola S. Nikolov, Aicha Majda

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Feature extraction has become a prerequisite step in computer vision problems, its importance resides in extracting significant hidden features from data, to help machine learning algorithms reach higher performance. Feature extraction techniques were behind the breakthrough in deep learning era, by providing relevant features. Deep learning architectures have overcome the state of the art in many different computer vision fields. In this work we are going to discuss and compare the accuracy of various global feature extraction methods, using deep learning for lung nodule detection. The experimental results show that feature extraction with convolutional neural networks (CNNs) outperforms the other methods including restricted boltzmann machines (RBMs).

Original languageEnglish
Title of host publicationProceedings - 2019 International Conference on Intelligent Systems and Advanced Computing Sciences, ISACS 2019
EditorsMajid Ben Yakhlef, Abderrahim Saaidi, Isamil Akharraz, Aziza El Ouazizi
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728148137
DOIs
Publication statusPublished - Dec 2019
Event3rd International Conference on Intelligent Systems and Advanced Computing Sciences, ISACS 2019 - Taza, Morocco
Duration: 26 Dec 201927 Dec 2019

Publication series

NameProceedings - 2019 International Conference on Intelligent Systems and Advanced Computing Sciences, ISACS 2019

Conference

Conference3rd International Conference on Intelligent Systems and Advanced Computing Sciences, ISACS 2019
Country/TerritoryMorocco
CityTaza
Period26/12/1927/12/19

Keywords

  • CNN
  • Deep learning
  • Feature extraction
  • Global feature extraction
  • Machine learning
  • RBM

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