Early detection and treatment of preneoplastic lesions represents an obvious option to reduce morbidity and mortality from lung malignancies. Until now, radiological detection, sputum cytology, and autofluorescence have shown limited effectiveness as screening methods. Novel technologies such as Narrow Band Imaging (NBI) are showing promising results, but new studies are still needed to evaluate their use as screening methods. Together with early detection, adequate methods of lesion treatment, such as argon plasma coagulation, are needed. This case report concerns a 45-year-old man who was referred for bronchoscopy after his annual checkup. Using NBI technology, a preneoplastic lesion was identified, and treated using argon plasma coagulation. Our experience has shown us that both NBI screening and argon plasma coagulation are very promising, easily implemented, methods.

In this paper, a wavelet-based neural network (WNN) classifier for recognizing EEG signals is implemented and tested under three sets EEG signals (healthy subjects, patients with epilepsy and patients with epileptic syndrome during the seizure). First, the Discrete Wavelet Transform (DWT) with the Multi-Resolution Analysis (MRA) is applied to decompose EEG signal at resolution levels of the components of the EEG signal (delta, theta, alpha, beta and gamma) and the Parsevals theorem are employed to extract the percentage distribution of energy features of the EEG signal at different resolution levels. Second, the neural network (NN) classifies these extracted features to identify the EEGs type according to the percentage distribution of energy features. The performance of the proposed algorithm has been evaluated using in total 300 EEG signals. The results showed that the proposed classifier has the ability of recognizing and classifying EEG signals efficiently.