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Segmentation of Skin Lesion towards Melanoma Skin Cancer Classification

http://hdl.handle.net/20.500.12678/0000005413
ca598ba6-35fe-4e12-ae77-f3f047e7c3b8
ded6fe46-6dc5-4f70-ac3d-e22671733496
Publication type
Journal article
Upload type
Publication
Title
Title Segmentation of Skin Lesion towards Melanoma Skin Cancer Classification
Language en
Publication date 2019-06-01
Authors
Nay Chi Lynn
Nu War
Description
Melanoma is one form of skin cancer which is one of the most hazardous types of cancer happened in people. Incidence of
skin cancer has been increasing over decades due to excess exposure of radiations from sun causing erosion to skin melanin. The
automatic detection of melanoma in dermatological images is a challenging task because of the diverse contrast of skin lesions, the
magnitude of melanoma within the class, and the utmost optical similarity to melanoma and lesions other than melanoma and the
beingness of many artifacts in the lesion pictures. In this work, the skin lesion analysis system to aid for the melanoma detection is
proposed. Firstly, the skin lesion from dermoscopy images is automatically segmented with the use of texture filters. Then, the features
according to the underlying ABCD dermatology rules are then extracted from the segmented skin lesion. Finally, the system is classified
by random subspace ensemble classifier in order to determine the images as benign or malignant melanoma
The performance of the study was experimented with their precision and it achieves with compromising results.
Keywords
Melanoma, Skin Cancer, Segmentation, Feature Extraction, Classification
Journal articles
Issue 3
International Journal of Computer Science and Network (IJCSN)
200-206
Volume 8
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