Computer-aided diagnosis of melanocytic skin tumors by use of confocal laser scanning microscopy images.

Objective: To check the applicability of machine learning algorithms for the computer-aided diagnosis of confocal laser scanning microscopy (CLSM) views of skin lesions.

Study Design: Features, based on spectral properties of the wavelet transform, are very suitable for the automatic analysis because architectural structures at different scales play an important role in diagnosis of CLSM views. The images are discriminated by several machine learning algorithms, based on Bayes-, tree-, rule-, function (numeric)-, and lazy-classifiers.