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.