EVALUATING FLUORESCENCE ILLUMINATION TECHNIQUES FOR SKIN LESION DIAGNOSIS

摘要

New illumination and imaging techniques are continually being developed for cancer diagnosis. They need to be evaluated in the framework of a specific diagnostic problem. In this work, we evaluate the usefulness of fluorescence illumination within the framework of skin cancer diagnosis. This illumination provides monochrome images that encode certain information about deep layers of the skin, which can be particularly interesting for the diagnosis of skin lesions such as basal cell carcinoma. A broad study of candidate diagnostic features extracted from fluorescence images and evaluated within the framework of the posed diagnostic problem was conducted. Afterward, we used both a genetic algorithm (GA) and forward and backward scanning methods for feature selection and evaluated the diagnostic results by using the K-nearest neighbors (KNN) classifier. This work validates the fluorescence illumination technique for skin cancer diagnosis, indicating concrete image processing techniques that best target the diagnostic problem, and shows that the GA approach obtains the best classification results.