Comparative Performance Analysis of State-of-the-Art Classification Algorithms Applied to Lung Tissue Categorization View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2008-11-04

AUTHORS

Adrien Depeursinge, Jimison Iavindrasana, Asmâa Hidki, Gilles Cohen, Antoine Geissbuhler, Alexandra Platon, Pierre-Alexandre Poletti, Henning Müller

ABSTRACT

In this paper, we compare five common classifier families in their ability to categorize six lung tissue patterns in high-resolution computed tomography (HRCT) images of patients affected with interstitial lung diseases (ILD) and with healthy tissue. The evaluated classifiers are naive Bayes, k-nearest neighbor, J48 decision trees, multilayer perceptron, and support vector machines (SVM). The dataset used contains 843 regions of interest (ROI) of healthy and five pathologic lung tissue patterns identified by two radiologists at the University Hospitals of Geneva. Correlation of the feature space composed of 39 texture attributes is studied. A grid search for optimal parameters is carried out for each classifier family. Two complementary metrics are used to characterize the performances of classification. These are based on McNemar’s statistical tests and global accuracy. SVM reached best values for each metric and allowed a mean correct prediction rate of 88.3% with high class-specific precision on testing sets of 423 ROIs. More... »

PAGES

18-30

References to SciGraph publications

  • 1986-03. Induction of decision trees in MACHINE LEARNING
  • 2006. Classification of Lung Disease Pattern Using Seeded Region Growing in AI 2006: ADVANCES IN ARTIFICIAL INTELLIGENCE
  • 2005-01-01. Weka in DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK
  • 2009. Weka-A Machine Learning Workbench for Data Mining in DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK
  • 1998-06. A Tutorial on Support Vector Machines for Pattern Recognition in DATA MINING AND KNOWLEDGE DISCOVERY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10278-008-9158-4

    DOI

    http://dx.doi.org/10.1007/s10278-008-9158-4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1042000615

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/18982390


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