Design and evaluation of neural classifiers application to skin lesion classification

Abstract

Addresses design and evaluation of neural classifiers for the problem of skin lesion classification. By using Gauss Newton optimization for the entropic cost function in conjunction with pruning by Optimal Brain Damage and a new test error estimate, the authors show that this scheme is capable of optimizing the architecture of neural classifiers. Furthermore, error-reject tradeoff theory indicates, that the resulting neural classifiers for the skin lesion classification problem are near-optimal.