A multiresolution support vector machine based algorithm for pneumoconiosis detection from chest radiographs | IEEE Conference Publication | IEEE Xplore

A multiresolution support vector machine based algorithm for pneumoconiosis detection from chest radiographs


Abstract:

We consider the problem of detecting the presence of pneumoconiosis in a patient on the basis of evidence found in chest radiographs. Abnormalities pertaining to pneumoco...Show More

Abstract:

We consider the problem of detecting the presence of pneumoconiosis in a patient on the basis of evidence found in chest radiographs. Abnormalities pertaining to pneumoconiosis appear in the form of opacities of various sizes; the profusion of these opacities determines the stage of the disease. We present a multiresolution approach whereby we segment regions of interest (ROIs) from the X-Ray image at two levels - lung field and lung zone. We characterize each of these regions using a set of features and build support vector machine (SVM) classifiers that can predict whether or not the region contains any abnormalities. We combine these ROI-level predictions with a second stage SVM in order to get a prediction for the entire chest. Experimental validation shows that this approach provides good results.
Date of Conference: 14-17 April 2010
Date Added to IEEE Xplore: 21 June 2010
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Conference Location: Rotterdam, Netherlands

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