Neela A G

Implementation of Support Vector Machine for Identification of Skin Cancer

Full Text (PDF, 879KB), PP.42-52

Views: 0 Downloads: 0

Author(s)

1. Department of Electronics and Communication Engineering JSS Academy of Technical Education, Bangalore, Karnataka, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijem.2019.06.04

Received: 21 Sep. 2019 / Revised: 2 Oct. 2019 / Accepted: 15 Oct. 2019 / Published: 8 Nov. 2019

Index Terms

Carcinoma, Dermoscopy, ANN, GLCM, SVM, Image Processing, Malignant Basal Cell Carcinoma (BCC), Squamous Cell Carcinoma (SCC).

Abstract

Skin cancer is one of the most death causing cancer with the increase of infections on skin due to various parameters of the nature, atmosphere and geographical area .The abnormal growth of skin cells has become common in today’s world this abnormal growth is termed as skin cancer. Skin cancer mostly develops on the part of the skin which is exposed to sun light continuously or for long duration develops on body exposed to sun light, but it can occur anywhere on the body. Skin cancer in beginning stage is curable. Patient’s life can be saving from skin cancer by early & fast detection. Early detection of skin cancer in achievable at beginning stage with the new technology. Biopsy method was used to detect the cancer in the earlier days. During biopsy, a small part of the skin tissue is extracted from the carcinoma patient; this part of the tissue will be processed in various laboratories for the identification of the presence of infected cells and the stage at which the cancer is in. Biopsy was a very time consuming and painful for the patients, and the result of biopsy process was not accurate and correct. To overcome the loner procedure and to increase the accuracy Support Vector Machine Algorithm was used in identifying the infection/ Carcinoma at the early stage and cure the infection before it leads to death.

Cite This Paper

Neela A G. "Implementation of Support Vector Machine for Identification of Skin Cancer", International Journal of Engineering and Manufacturing(IJEM), Vol.9, No.6, pp.42-52, 2019. DOI: 10.5815/ijem.2019.06.04

Reference

[1]C.Nageswara Rao, S.SreehariSastry and K.B.Mahalakshmi “Co-Occurrence Matrix and Its Statistical Features an Approach for Identification of Phase Transitions of Mesogens”, International Journal of Innovative Research in Engineering and Technology, Vol. 2, Issue 9, September 2013.

[2]Santosh Achakanalli& G. Sadashivappa ,” Statistical Analysis of Skin Cancer Image –A Case Study “ , International Journal of Electronics and Communication Engineering (IJECE), Vol. 3, Issue 3, May 2014.

[3]“Digital image processing” by jayaraman. Page 244,254-247,270-273. (Gray level, median filter).

[4]Algorithm For Image Processing And Computer Vision .Page 142-145. (Thresholding)

[5]Kawsar Ahmed, TasnubaJesmin, “Early Prevention and Detection of Skin Cancer Risk using Data Mining”, International Journal of Computer Applications, Volume 62– No.4, January 2013.

[6]Maurya R, Surya K.S,"GLCM and Multi Class Support Vector Machine based Automated Skin Cancer Classification, "IEEE journal, vol 12, 2014.

[7]A.A.L.C. Amarathunga,” Expert System for Diagnosis of Skin Diseases”, International Journal of Scientific & Technology Research, Volume 4, Issue 01, 2015.

[8]Mariam A.Sheha,”Automatic Detection of Melanoma Skin Cancer”, International Journal of Computer Applications, 2012.

[9]Anshubharadwaj, “Support Vector Machine”, Indian Agriculture Statistics Research Institute.

[10]M.Chaithanya Krishna, S.Ranganayakulu, “Skin Cancer Detection and Feature Extraction through Clustering Technique”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 4, Issue 3, March 2016.

[11]Dr.J.Abdul Jaleel ,Sibi Salim and Aswin R.B. “Artificial Neural Network Based Detection of Skin

[12]Cancer”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering,Vol. 1, Issue 3, September 2012.

[13]Anal Kumar Mittra and Dr.Ranjan Parekh, “Automated Detection of Skin Diseases” International Journal of Engineering Science and Technology (IJEST) Vol. 3 No. 6 June 2011.

[14]Catarina Barata, Margarida Ruela , Mariana Francisco, Teresa Mendonca and Jorge S. Marques, “Two Systems for the Detection of Melanomas in Dermoscopy Images Using Texture and Color Features”, IEEE SYSTEMS JOURNAL 2013.

[15]Md.Amran Hossen Bhuiyan, Ibrahim Azad, Md.Kamal Uddin, Image Processing for Skin Cancer Features Extraction, International Journal of Scientific and Engineering Research Volume 4, Issue 2, ISSN 2229-5518, February-2013.

[16]A.Aswini, E.Cirimala, R.Ezhilarasi, M.Jayapratha, Non Invasive Screening and Discrimination of Skin Images For Early Melanoma Detection, International Journal of scientific research and management (IJSRM), Volume, 2, Issue, 4, Pages 781- 786, 2013

[17]Arati P. Chavan D. K. Kamat Dr. P. M. Patil, CLASSIFICATION OF SKIN CANCERS USING IMAGE PROCESSING, International Journal of Advance Research in Electronics, Electrical Computer Science Applications of Engineering Technology Volume 2, Issue 3, , PP 378-384 June 2014.

[18]Chaithanya Krishna, S.Ranganayakulu, “Skin Cancer Detection and Feature Extraction through Clustering Technique”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 4, Issue 3, March 2016.

[19]J Abdul Jaleel, Sibi Salim, Aswin.R.B,” Computer Aided Detection 01 Skin Cancer”, International Conference on Circuits, Power and Computing Technologies, 2013.

International Journal of Engineering and Manufacturing (IJEM)