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Computer Vision for Skin Cancer Diagnosis and Recognition using RBF and SOM
Abrham Debasu Mengistu, Dagnachew Melesew Alemayehu
Pages - 311 - 319     |    Revised - 30-11-2015     |    Published - 31-12-2015
Volume - 9   Issue - 6    |    Publication Date - November / December 2015  Table of Contents
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KEYWORDS
SOM, RBF, KNN, Digital Image Processing, Dermofit.
ABSTRACT
Human skin is the largest organ in our body which provides protection against heat, light, infections and injury. It also stores water, fat, and vitamin. Cancer is the leading cause of death in economically developed countries and the second leading cause of death in developing countries. Skin cancer is the most commonly diagnosed type of cancer among men and women. Exposure to UV rays, modernize diets, smoking, alcohol and nicotine are the main cause. Cancer is increasingly recognized as a critical public health problem in Ethiopia. There are three type of skin cancer and they are recognized based on their own properties. In view of this, a digital image processing technique is proposed to recognize and predict the different types of skin cancers using digital image processing techniques. Sample skin cancer image were taken from American cancer society research center and DERMOFIT which are popular and widely focuses on skin cancer research. The classification system was supervised corresponding to the predefined classes of the type of skin cancer. Combining Self organizing map (SOM) and radial basis function (RBF) for recognition and diagnosis of skin cancer is by far better than KNN, Naïve Bayes and ANN classifier. It was also showed that the discrimination power of morphology and color features was better than texture features but when morphology, texture and color features were used together the classification accuracy was increased. The best classification accuracy (88%, 96.15% and 95.45% for Basal cell carcinoma, Melanoma and Squamous cell carcinoma respectively) were obtained using combining SOM and RBF. The overall classification accuracy was 93.15%.
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1 http://www.cancer.org/cancer/skincancer
2 Cancer in Africa, World Health Organization, International Agency for Research on cancer; 2009.
3 http://www.skincancer.org/skin-cancer-information/skin-cancer-facts
4 http://www.cdc.gov/cancer/skin/statistics/
5 http://www.skincancer.org/skin-cancer-information/skin-cancer-facts
6 World Health Organization, the global burden of disease: 2008 Update Geneva: World Health Organization,2008
7 A Morrone, Skin Cancer in Ethiopia,21st world congress of dermatology, 2007
8 http://www.skincancer.org/skin-cancer-information/skin-cancer-facts
9 William K. Pratt: Digital image processing, PIKS Scientific inside, John Wiley, 4th Edition, 2007
10 John Breneman towards early stages of malignant melanoma detection Using Consumer Mobile Devices
11 Luís Filipe Caeiro M argalho Guerra Rosado, Automatic System for Diagnosis of Skin Lesions Based on Dermoscopic Images
12 T Y Satheesha,, Dr. D Sathya Narayana, Dr. M N Giriprasad, review on early detection of melanoma:2012
13 Ilias Maglogiannis, Dimitrios I. Kosmopoulos, Computational vision systems for the detection of malignant melanoma
14 Ioana Dumitrache, Alina Elena Sultana, and Radu Dogaru Automatic Detection of Skin Melanoma from Images using Natural Computing Approaches
15 Aswin.R.B, J. Abdul Jaleel, Sibi Salim, Implementation of ANN Classifier using MATLAB for Skin Cancer Detection,2013
16 Ioana Dumitrache, Alina Elena Sultana, and Radu Dogaru Automatic Detection of Skin Melanoma from Images using Natural Computing Approaches
17 Nilkamal S. Ramteke and Shweta V. Jain ABCD rule based automatic computer-aided skin cancer detection using MATLAB
18 Santosh Achakanalli and G. Sadashivappa Skin Cancer Detection and Diagnosis Using Image Processing and Implementation Using Neural Networks and ABCD Parameters
19 Mariam A.Sheha and Mai S.Mabrouk Automatic Detection of Melanoma Skin Cancer using Texture Analysis
20 Catarina Barata, Margarida Ruela, Mariana Francisco, Teresa Mendonça, and Jorge S. Marques Detection of Melanomas in Dermoscopy Images Using Texture and Color Features
21 Santosh Achakanalli & G. Sadashivappa, Skin Cancer Detection And Diagnosis Using Image Processing And Implementation Using Neural Networks And ABCD Parameters
22 Luís Filipe Caeiro M argalho Guerra Rosado, Automatic System for Diagnosis of Skin Lesions Based on Dermoscopic Images
23 Mariam A. Sheha , Mai S. Mabrouk , Amr Sharawy, Automatic Detection of Melanoma Skin Cancer using Texture Analysis
24 Lin Li, Qizhi Zhang, Yihua Ding, Huabei Jiang, Bruce H Thiersand, Automatic diagnosis of melanoma using machine learning methods
25 Sarika Choudhari, Seema Biday, Artificial Neural Network for SkinCancer Detection
26 Dr. J. Abdul Jaleel, Sibi Salim, Aswin.R.B, Diagnosis and Detection of Skin Cancer Using Artificial Intelligence
27 Peyman Sabouri, Hamid GholamHosseini, John Collins, Border Detection of Mela noma Skin Lesions, 2013
28 Dr. J. Abdul Jaleel, Sibi Salim, Aswin.R.B, Artificial Neural Network Based Detection of Skin Cancer,2012
29 Snehal Salunke, Survey on Skin lesion segmentation and classification,2014
30 Nandini M.N., M.S.Mallikarjunaswamy, Detection of Melanoma Skin Disease using Dermoscopy Images
31 Tinku Acharya and Ajoy K. Ray, Image Processing Principles and Applications, Jhon Wiley, 2005.
32 William K. Pratt: Digital image processing, PIKS Scientific inside, John Wiley, 4th Edition, 2007.
Mr. Abrham Debasu Mengistu
Bahir Dar University - Ethiopia
abrhamd@bdu.edu.et
Mr. Dagnachew Melesew Alemayehu
Bahir Dar University - Ethiopia