Research on SVM and KNN Classifiers for Skin Cancer Detection
A.Murugan1, S.Anu H Nair2, K.P. Sanal Kumar3
1A.Murugan, Research Scholar, Dept. Of Computer and Information Sciences, Annamalai University, Annamalainagar, Tamil Nadu. India.
2Dr.S.Anu H Nair, Asst.Professor, Dept. of CSE, Annamalai University, (Deputed to WPT, Chennai), Tamil Nadu, India.
3Dr. K.P. Sanal Kumar, Asst. ProfessorDept. of Computer ScienceR.V.Govt. Arts College,Chengalpattu, Tamil Nadu, India.
Manuscript received on November 28, 2019. | Revised Manuscript received on December 30, 2019. | Manuscript published on December 30, 2019. | PP: 4627-4632 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B5117129219/2019©BEIESP | DOI: 10.35940/ijeat.B5117.129219
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Generally, a not unusual skin ailment in human disorder. In laptop imaginative and prescient applications, coloration is a sturdy indication for this sickness. This machine identifies pores and skin cancer based totally on the picture of the pores and skin. Initially, the skin image is filtered using filters and segmented Gausian the use of energetic contour segmentation. Segmented pix are fed as an input to the feature extraction. Pictures extracted classified the use of class strategies such as Support Vector Machine classifiers(SVM) and k Nearest Neighbor(kNN) classifiers. SVM classifier provided better results than kNN classifier
Keywords: Melanoma, Skin cancer, Gausian filter, Active contour.