Theoretical Interpretations and Applications of Radial Basis Function Networks

Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains

Exploiting Classifier Combination for Early Melanoma Diagnosis Support

Melanoma is the most dangerous skin cancer and early diagnosis is the main factor for its successful treatment. Experienced dermatologists with specific training make the diagnosis by clinical inspection and they reach 80% level of both sensitivity and specificity. In this paper, we present a multi-classifiers system for supporting the early diagnosis of melanoma. The system acquires a digital image of the skin lesion and extracts a set of geometric and colorimetric features. The diagnosis is performed on the vector of features by integrating with a voting schema the diagnostic outputs of three different classifiers: discriminant analysis, k-nearest neighbor and decision tree. The system is build and validated on a set of 152 skin images acquired via D-ELM. The results are comparable or better of the diagnostic response of a group of expert dermatologist