Open AccessAn Empirical Study on Image Segmentation Techniques for Detection of Skin Cancer
Author(s) -
M. Kavitha,
Arunkumar Arumugam,
V. Saravana Kumar,
S. Anantha Sivaprakasam
Publication year - 2021
Publication title -
journal of pharmaceutical research international
Language(s) - English
Resource type - Journals
ISSN - 2456-9119
DOI - 10.9734/jpri/2021/v33i1031235
Subject(s) - skin cancer , segmentation , hazard , shading , dermatology , malignancy , medicine , cancer , computer science , artificial intelligence , pathology , biology , ecology , computer graphics (images)
Skin cancer is a crucial predicament in most of western countries including Europe, Australia and America. It is quite often curable whenever perceived and treated early. The significant hazard factors related are skin shading, deficiency of sun-lights, atmosphere, age, and hereditary. The most ideal approach to distinguish melanoma is to perceive another spot in the skin or recognize that is fluctuating in size, shape and shading. Early detection of skin malignancy can stay away from death. Finding of the skin ailment relies upon the extraction of the anomalous skin locale. Right now, methods to separate the skin injury districts are proposed and their outcomes are looked at dependent on the measurable and surface properties. In this study, the myriad kind of features of Dermoscopy image analysis has been thoroughly explores. Moreover, disparity segmentation techniques for detecting Melanoma Skin Cancer are discussed. The ultimate aim of this discussion is to provide suggestions for carrying a future research based about this relevance and limitations.