Wavelet analysis for identification of lung abnormalities using artificial neural network

This research analyzed the use of daubechies wavelet as a feature extraction and confusion matrix as the principal parameter of accuracy percentage level in neural network. Detection process began with image pre-processing, lung area segmentation, feature extraction, and training phase. Classifications of the system output consisted of normal lung, pleural effusion, and pulmonary tuberculosis. Seventy five amounts of thorax samples were used as training data and thirty five thoraxes were used as test data. The experiment results showed that the decomposition at level 7 with order db6 was the best configuration for feature extraction which attained up to 91.65% of accuracy.