{"_buckets": {"deposit": "ded6fe46-6dc5-4f70-ac3d-e22671733496"}, "_deposit": {"created_by": 45, "id": "5413", "owner": "45", "owners": [45], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "recid", "value": "5413"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/5413", "sets": ["user-uit"]}, "communities": ["uit"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Segmentation of Skin Lesion towards Melanoma Skin Cancer Classification", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Melanoma is one form of skin cancer which is one of the most hazardous types of cancer happened in people. Incidence of\nskin cancer has been increasing over decades due to excess exposure of radiations from sun causing erosion to skin melanin. The\nautomatic detection of melanoma in dermatological images is a challenging task because of the diverse contrast of skin lesions, the\nmagnitude of melanoma within the class, and the utmost optical similarity to melanoma and lesions other than melanoma and the\nbeingness of many artifacts in the lesion pictures. In this work, the skin lesion analysis system to aid for the melanoma detection is\nproposed. Firstly, the skin lesion from dermoscopy images is automatically segmented with the use of texture filters. Then, the features\naccording to the underlying ABCD dermatology rules are then extracted from the segmented skin lesion. Finally, the system is classified\nby random subspace ensemble classifier in order to determine the images as benign or malignant melanoma\nThe performance of the study was experimented with their precision and it achieves with compromising results."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Melanoma"}, {"interim": "Skin Cancer"}, {"interim": "Segmentation"}, {"interim": "Feature Extraction"}, {"interim": "Classification"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_no", "licensefree": "Copyright (c) 2019 International Journal of Computer Science and Network", "licensetype": "license_free"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "Issue 3", "subitem_journal_title": "International Journal of Computer Science and Network (IJCSN)", "subitem_pages": "200-206", "subitem_volume": "Volume 8"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Nay Chi Lynn"}, {"subitem_authors_fullname": "Nu War"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Journal article"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2019-06-01"}, "item_title": "Segmentation of Skin Lesion towards Melanoma Skin Cancer Classification", "item_type_id": "21", "owner": "45", "path": ["1596102391527"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000005413", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2020-09-16"}, "publish_date": "2020-09-16", "publish_status": "0", "recid": "5413", "relation": {}, "relation_version_is_last": true, "title": ["Segmentation of Skin Lesion towards Melanoma Skin Cancer Classification"], "weko_shared_id": -1}