Assessment of Segmentation techniques for skin cancer detection

Abstract

Skin cancer appears to be the most common among all tumors throughout the globe. The initial finding of
skin cancer can be alleviated. Late detection leads to fatal. A human inquiry is thought-provoking. The
biopsy procedure is agonizing, so computerized examination of skin cancer turns out to be noteworthy. A
prevalent literature survey is carried out to study the State-of-art procedures for skin cancer diagnosis.
Segmentation of skin lesion is a crucial task due to several features like the existence of hair, illumination
difference, irregular skin color, and multiple unnatural skin regions. This paper recommends a
comparison of various segmentation techniques and k-means clustering algorithms to segment the lesion.
Several methodologies have been anticipated to determine skin cancer. The features can be resolved by
familiarizing an advanced method for segmenting the skin lesion from macroscopic images based on the
discrete wavelet transformation.