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

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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.

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Acknowledgments

We thank Dr. Mélanie Hilario for her valuable comments on the methodology for benchmarking the classifiers. This work was supported by the Swiss National Science Foundation (FNS) with grant 200020-118638/1, the equalization fund of University and Hospitals of Geneva (grant 05-9-II), and the EU 6th Framework Program in the context of the KnowARC project (IST 032691).

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Authors and Affiliations

  1. Service of Medical Informatics, Geneva University Hospitals and University of Geneva, 24, rue Micheli-du-Crest, CH-1211, Geneva 14, Switzerland

    Adrien Depeursinge, Jimison Iavindrasana, Asmâa Hidki, Gilles Cohen, Antoine Geissbuhler & Henning Müller

  2. Service of Emergency Radiology, Geneva University Hospitals and University of Geneva, 24, rue Micheli-du-Crest, CH-1211, Geneva 14, Switzerland

    Alexandra Platon & Pierre-Alexandre Poletti

  3. Business Information Systems, University of Applied Sciences, Sierre, Switzerland

    Henning Müller

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  1. Adrien Depeursinge
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  2. Jimison Iavindrasana
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  4. Gilles Cohen
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  8. Henning Müller
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Correspondence to Adrien Depeursinge.

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Depeursinge, A., Iavindrasana, J., Hidki, A. et al. Comparative Performance Analysis of State-of-the-Art Classification Algorithms Applied to Lung Tissue Categorization. J Digit Imaging 23, 18–30 (2010). https://doi.org/10.1007/s10278-008-9158-4

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  • DOI: https://doi.org/10.1007/s10278-008-9158-4

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