Objective To investigate the early diagnosis of pneumoconiosis on digital radiographs by means of wavelet transform-derived entropy and the related technologies of classification.Methods Wavelet transform-derived entropies were extracted from the digital X-ray radiographies(DRs) of 70 normal persons and 40 pneumoconiosis patients and were selected by decision tree.Support vector machines(SVMs) with different kernel functions were adopted to distinguish pneumoconiosis DRs from normal DRs.The classification performance was estimated and evaluated through 5-fold cross validation.Results The DR images were wavelet-discomposed for 8 times,resulting in 8 wavelet entropies to form the feature full-set,and six were selected to form the feature subset.The classification performances based on the feature subset were better than those based on the feature full-set when classification was done with SVMs.SVM with linear kernel function performed better than SVMs with polynomial and Gauss kernel functions,with accuracy of 84.6% and an area under the ROC curve of 0.88±0.04.Conclusions The early diagnosis of pneumoconiosis based on wavelet transform-derived texture features with SVM is of a high level.