Paper Title
A FPGA Implementation On Skin Cancer Detection Using TDLS Algorithm

Melanoma is one of the deadliest forms of skin cancer if it is left untreated. Incidence rates of melanoma have been increasing, especially among adults, but survival rates are high if detected early. The time and costs required for the patients to go for dermatologist for melanoma are prohibitively expensive. One challenge in implementing such a system is locating the skin lesion in the digital image. The goal of this paper is to develop a framework that automatically correct and segment the skin lesion from an input photograph. The first part of this paper is to model illumination variation using a proposed multi-stage illumination modelling algorithm and then using that model to correct the original photograph. Second, a set of representative texture distributions are learned from the corrected photograph and texture distinctiveness metric is calculated for each distribution. Finally, a texture-based segmentation algorithm classifies regions in the photograph as normal skin or lesion based on the occurrence of representative texture distributions. The resulting segmentation can be used as an input to separate feature extraction and melanoma classification algorithms. Index Terms— Melanoma, Skin Cancer, Segmentation, Texture, Neural Network.