Skin Lesion Diagnosis Using Fluorescence Images

This paper presents a computer aided diagnosis system for skin lesions. Diverse parameters or features extracted from fluorescence images are evaluated for cancer diagnosis. The selection of parameters has a significant effect on the cost and accuracy of an automated classifier. The genetic algorithm (GA) performs parameters selection using the classifier of the K-nearest neighbours (KNN). We evaluate the classification performance of each subset of parameters selected by the genetic algorithm. This classification approach is modular and enables easy inclusion and exclusion of parameters. This facilitates the evaluation of their significance related to the skin cancer diagnosis. We have implemented this parameter evaluation scheme adopting a strategy that automatically optimizes the K-nearest neighbours classifier and indicates which features are more relevant for the diagnosis problem.

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