Improvement of computational efficiency using a cascade classification scheme for the classification of diffuse infiltrative lung disease on HRCT

Youngjoo Lee; Namkug Kim; Joon Beom Seo M.D.; Sang Ok Park M.D.; Young Kyung Lee; Suk Ho Kang

doi:10.1117/12.811439

27 February 2009 Improvement of computational efficiency using a cascade classification scheme for the classification of diffuse infiltrative lung disease on HRCT

Youngjoo Lee, Namkug Kim, Joon Beom Seo M.D., Sang Ok Park M.D., Young Kyung Lee, Suk Ho Kang

Author Affiliations +

Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 72603A (2009) https://doi.org/10.1117/12.811439
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

In this paper, a cascade classification scheme was proposed to improve computational efficiency in lung parenchyma quantification in HRCT images. Proposed cascade classification scheme includes four steps: cost-based class-specific feature selection, class-specific classifier training, classifier ordering, cascade feature extraction and classification. In the first step, feature sets were determined by sequential forward floating selection (SFFS) using performance improvement to extraction cost ratio criterion. Then classifiers were trained to classify specific class from all of other classes. Using accuracies of those classifiers, the order of classification was determined; from the highest accuracy to lowest accuracy. To quantify new images, feature extraction and classification were sequentially repeated. The impact of using the proposed cascade classification scheme is evaluated in terms of computational cost and classification accuracy. For automated classification, support vector machine (SVM) was implemented. To assess the performance and crossvalidation of the system, ten-folding method was used. In the experimental results, the computational cost was reduced by 46% and the overall accuracy was 92.04% which is not significantly different in a comparison of conventional method. This work shows that, in our classification problem, using the proposed cascade classification scheme can reduce the computational cost in the feature extraction while maintaining the classification accuracy.

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Youngjoo Lee, Namkug Kim, Joon Beom Seo M.D., Sang Ok Park M.D., Young Kyung Lee, and Suk Ho Kang "Improvement of computational efficiency using a cascade classification scheme for the classification of diffuse infiltrative lung disease on HRCT", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72603A (27 February 2009); https://doi.org/10.1117/12.811439

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KEYWORDS

Feature extraction

Feature selection

Image classification

Lung

Binary data

Opacity

Classification systems

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