DocumentCode
606123
Title
Computer Aided Detection of Skin Cancer
Author
Jaleel, J. Abdul ; Salim, Sibi ; Aswin, R.B.
Author_Institution
Dept. of Electr. & Electron. Eng., TKM Coll. of Eng., Kollam, India
fYear
2013
fDate
20-21 March 2013
Firstpage
1137
Lastpage
1142
Abstract
Skin cancers are the most common form of cancers in humans. It is a deadly type of cancer affecting skin. Most of the skin cancers are curable at initial stages. So an early detection of skin cancer can save the patients. Conventional diagnosis method for skin cancer detection is Biopsy method. It is done by removing or scraping off skin and that sample undergoes a series of laboratory testing. It is painful and time consuming one. Computer based skin cancer detection is more advantageous to patients, by which patients can identify the skin cancer without going to hospital or without the help of a doctor. Computer based detection uses imaging techniques and Artificial Intelligence. The different stages of detection involves- collection of dermoscopic images, filtering the images for removing hairs and noises, segmenting the images using Maximum Entropy Threshold, feature extraction using Gray Level Co-occurrence Matrix(GLCM), and classification using Artificial Neural Network(ANN). Back-Propagation Neural (BPN) Network is used for classification purpose. It classifies the given data set into cancerous or non-cancerous.
Keywords
artificial intelligence; biomedical optical imaging; cancer; computer aided analysis; entropy; feature extraction; image classification; image segmentation; medical image processing; neural nets; skin; artificial intelligence; artificial neural network; back-propagation neural network; biopsy method; computer aided detection; conventional diagnosis method; dermoscopic images; feature extraction; filtering; gray level co-occurrence matrix; image classification; image segmenatation; imaging techniques; maximum entropy threshold; skin cancer; skin scraping; Biomedical imaging; Cancer; Feature extraction; Image segmentation; Iron; Training; Artificial Neural Network; Gray Level Co-occurrence Matrix; Segmentation; Skin cancer;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits, Power and Computing Technologies (ICCPCT), 2013 International Conference on
Conference_Location
Nagercoil
Print_ISBN
978-1-4673-4921-5
Type
conf
DOI
10.1109/ICCPCT.2013.6528879
Filename
6528879
Link To Document