Design and evaluation of neural classifiers application to skin lesion classification

We address 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, we 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. 1 INTRODUCTION Melanoma is the most lethal of skin cancers. However, patients may be saved from this life threatening cancer if their lesion is detected at an early stage. Computer imaging may assist and improve the detection of such early lesions. The "State of the art" in this field was recently reviewed in an editorial in the journal "Computerized Medical Imaging and Graphics" [1]. Although applied to the problem of skin lesion classification, the main objective of this paper is to introduce and app..