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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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Mr. Abrham Debasu Mengistu
Bahir Dar University - Ethiopia
abrhamd@bdu.edu.et
Mr. Dagnachew Melesew Alemayehu
Bahir Dar University - Ethiopia