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Comparative performance analysis of state-of-the-art classification algorithms applied to lung tissue categorization.

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-near...

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Published in:Journal of Digital Imaging Vol. 23; no. 1; pp. 18 - 31
Main Authors: Depeursinge A, Iavindrasana J, Hidki A, Cohen G, Geissbuhler A, Platon A, Poletti P, Müller H
Format: diagnostic images equations & formulas pictorial research tables/charts Journal Article
Published: Springer Nature Feb2010
Online Access:View this record in EBSCOhost
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      jtl: Journal of Digital Imaging
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      dt: Feb2010
      vid: 23
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      pub: Springer Nature
      place: New York, New York
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        2010539533
        10.1007/s10278-008-9158-4
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        atl: Comparative performance analysis of state-of-the-art classification algorithms applied to lung tissue categorization.
      aug:
        au:
          Depeursinge A
          Iavindrasana J
          Hidki A
          Cohen G
          Geissbuhler A
          Platon A
          Poletti P
          Müller H
        affil: Service of Medical Informatics, Geneva University Hospitals and University of Geneva, 24, rue Micheli-du-Crest CH-1211, Geneva 14, Switzerland
      sug:
        subj:
          Classification Algorithms Evaluation
          Diagnosis, Computer Assisted
          Lung Diseases, Interstitial Classification
          Comparative Studies
          Decision Trees
          Evaluation Research
          Funding Source
          Human
          McNemar's Test
          Radiography, Thoracic
      ab: 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.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        equations & formulas
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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