DocumentCode :
1776558
Title :
Hybrid genetic algorithm — Artificial neural network classifier for skin cancer detection
Author :
Aswin, R.B. ; Jaleel, J. Abdul ; Salim, Sibi
Author_Institution :
Dept. of Electr. & Electron. Eng., Mar Baselios Coll. of Eng. & Technol., Trivandrum, India
fYear :
2014
fDate :
10-11 July 2014
Firstpage :
1304
Lastpage :
1309
Abstract :
Skin cancer is a deadly form of cancer, affecting skin. It is one of the very deadly form of cancer. It occurs on the melanocytic cells of skin. So skin cancer is also known as Melanoma. Due to the malignancy feature it is also known as malignant melanoma. Melanoma causes the abnormal growth of melanocytic cells which produces the sun protective pigment melanin. Due to that reason melanoma appears as black or brown in color. Main factors that cause melanoma are over exposure to sunlight and genetic factors. Ultraviolet (UV) radiation from the sun is important for maintaining vitamin D levels in the body, but too much UV can cause sunburn, premature ageing, skin and damage and ultimately skin cancer. Early detection can cure skin cancer completely. The probability of cure decreases if it is not treated at early stages. Diagnosing methods like Biopsy, SIAscopy are painful and time consuming. An economical, less time consuming, painless Computer Aided Skin Cancer detection is proposed here. This methodology uses Digital Image processing and Artificial intelligence for skin cancer detection. This methodology involves no direct contact with skin. Only the dermoscopic image is used here. The image after certain image processing techniques is subjected to segmentation. After segmentation, the unique features are extracted from the image using feature extraction techniques. The feature extraction technique used here is GLCM(Gray Level Co-occurrence Matrix) and RGB color feature. These features are used for classification. Artificial neural network classifier is used for classification. In order to improve the accuracy of classification, the ANN is optimized by Genetic Algorithm.
Keywords :
cancer; feature extraction; genetic algorithms; image classification; image colour analysis; image segmentation; matrix algebra; medical image processing; neural nets; object detection; skin; ANN; GLCM feature; RGB color feature; SIAscopy; artificial intelligence; artificial neural network classifier; biopsy; computer aided skin cancer detection; cure probability; dermoscopic image; digital image processing; feature extraction; genetic factors; gray level co-occurrence matrix; hybrid genetic algorithm; image segmentation; malignant melanoma; melanocytic cells; red-green-blue color; skin cancer detection; sun protective pigment melanin; sunlight exposure; ultraviolet radiation; Feature extraction; Image segmentation; Lesions; Malignant tumors; Skin; Skin cancer; Artificial neural network; Genetic Algorithm; Gray Leval Cooccurrence Matrix; Melanoma; dermoscopic image;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 2014 International Conference on
Conference_Location :
Kanyakumari
Print_ISBN :
978-1-4799-4191-9
Type :
conf
DOI :
10.1109/ICCICCT.2014.6993162
Filename :
6993162
Link To Document :
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