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.