NDLI: Hybrid genetic algorithm — Artificial neural network classifier for skin cancer detection

Content Provider	IEEE Xplore Digital Library
Author	Aswin, R.B. Jaleel, J.A. Salim, S.
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. & Electron. Eng., Mar Baselios Coll. of Eng. & Technol., Trivandrum, India (Aswin, R.B.) \|\| Dept. of Electr. & Electron. Eng., TKM Coll. of Eng., Kollam, India (Jaleel, J.A.)
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.
Starting Page	1304
Ending Page	1309
File Size	721920
Page Count	6
File Format	PDF
ISBN	9781479941919
e-ISBN	9781479941902
DOI	10.1109/ICCICCT.2014.6993162
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-10
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Malignant tumors Gray Leval Cooccurrence Matrix Genetic Algorithm Melanoma Feature extraction Dermoscopic image Skin Artificial neural network Lesions Skin cancer
Content Type	Text
Resource Type	Article

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