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. RESULTS: The function and lazy classifiers delivered best classification results. However, these algorithms deliver no information about the inference mechanism leading to the classification. The tree classifiers provided better results than the rule classifiers. To obtain more insight into the inference process, and to compare it with the diagnostic guidelines of the dermopathologists, we combined the advantages of tree, numerical, and rule classifiers and choose the classification and regression trees (CART) algorithm, which automatically generates accurate inferring rules. The classification results were relocated to the images by use of the inferring rules as diagnostic aid. CONCLUSION: The discriminated elements of the skin lesions images show tissue with features in good accordance with typical diagnostic CLSM features.
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. RESULTS: The function and lazy classifiers delivered best classification results. However, these algorithms deliver no information about the inference mechanism leading to the classification. The tree classifiers provided better results than the rule classifiers. To obtain more insight into the inference process, and to compare it with the diagnostic guidelines of the dermopathologists, we combined the advantages of tree, numerical, and rule classifiers and choose the classification and regression trees (CART) algorithm, which automatically generates accurate inferring rules. The classification results were relocated to the images by use of the inferring rules as diagnostic aid. CONCLUSION: The discriminated elements of the skin lesions images show tissue with features in good accordance with typical diagnostic CLSM features.