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

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.