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Detection of Melanoma Skin Cancer using Segmentation and Classification Algorithms

Published on September 2015 by I.s. Akila, Sumathi V.
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National Conference on Information and Communication Technologies
Foundation of Computer Science USA
NCICT2015 - Number 2
September 2015
Authors: I.s. Akila, Sumathi V.
2bef02ad-af19-4e26-8448-203bfa560813

I.s. Akila, Sumathi V. . Detection of Melanoma Skin Cancer using Segmentation and Classification Algorithms. National Conference on Information and Communication Technologies. NCICT2015, 2 (September 2015), 1-4.

@article{
author = { I.s. Akila, Sumathi V. },
title = { Detection of Melanoma Skin Cancer using Segmentation and Classification Algorithms },
journal = { National Conference on Information and Communication Technologies },
issue_date = { September 2015 },
volume = { NCICT2015 },
number = { 2 },
month = { September },
year = { 2015 },
issn = 0975-8887,
pages = { 1-4 },
numpages = 4,
url = { /proceedings/ncict2015/number2/22351-1542/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Information and Communication Technologies
%A I.s. Akila
%A Sumathi V.
%T Detection of Melanoma Skin Cancer using Segmentation and Classification Algorithms
%J National Conference on Information and Communication Technologies
%@ 0975-8887
%V NCICT2015
%N 2
%P 1-4
%D 2015
%I International Journal of Computer Applications
Abstract

Melanoma is the most dangerous skin cancer. It should be diagnosed early because of its aggressiveness. To diagnose melanoma earlier, skin lesion should be segmented accurately. To reduce the cost for specialists to screen every patient, there is a need of automated melanoma prescreening system to diagnose melanoma using images acquired in digital cameras. In this frame work, an automated melanoma prescreening system is proposed to diagnose melanoma skin cancer using Modified TDLS algorithm and SVM classifier. Representative texture distributions are obtained from texture vectors. The segmentation accuracy is improved by modification in TDLS algorithm. TD metric is calculated with lesion texture distributions only. The entire system is tested using MATLAB software.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Melanoma Skin Lesion Tdls Svm Dermatoscope