{"_buckets": {"deposit": "65530c8f-ac50-4910-b407-66bc43264153"}, "_deposit": {"created_by": 45, "id": "5412", "owner": "45", "owners": [45], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "recid", "value": "5412"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/5412", "sets": ["user-uit"]}, "communities": ["uit"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Segmentation and Classification of Skin Cancer Melanoma from Skin Lesion Images", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Melanoma, one type of skin cancer is considered o\nthe most dangerous form of skin cancer occurred in humans.\nHowever it is curable if the person detects early. To minimize\nthe diagnostic error caused by the complexity of visual\ninterpretation and subjectivity, it is important to develop a\ntechnology for computerized image analysis. This paper\npresents a methodological approach for the classification of\npigmented skin lesions in dermoscopic images. Firstly, the image\nof the skin to remove unwanted hair and noise, and then the\nsegmentation process is performed to extract the affected area.\nFor detecting the melanoma skin cancer, the meanshift\nalgorithm that segments the lesion from the entire image is used\nin this study. Feature extraction is then performed by\nunderlying ABCD dermatology rules. After extracting the\nfeatures from the lesion, feature selection algorithm has been\nused to get optimized features in order to feed for classification\nstage. Those selected optimized features are classified using\nkNN, decision tree and SVM classifiers. The performance of the\nsystem was tested and compare those accuracies and get\npromising results."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Melanoma"}, {"interim": "Skin Cancer"}, {"interim": "Segmentation"}, {"interim": "Classification"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_no", "licensefree": "©2017 IEEE", "licensetype": "license_free"}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "PDCAT’17", "subitem_c_date": "18-20 December, 2017", "subitem_conference_title": "18th International Conference on Parallel and Distributed Computing, Applications and Technologies", "subitem_place": "Taipei, Taiwan", "subitem_website": "https://ieeexplore.ieee.org/document/8327076"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Nay Chi Lynn"}, {"subitem_authors_fullname": "Zin Mar Kyu"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Conference paper"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2017-12-20"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "10.1109/pdcat.2017.00028"}, "item_title": "Segmentation and Classification of Skin Cancer Melanoma from Skin Lesion Images", "item_type_id": "21", "owner": "45", "path": ["1596102391527"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000005412", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2020-09-16"}, "publish_date": "2020-09-16", "publish_status": "0", "recid": "5412", "relation": {}, "relation_version_is_last": true, "title": ["Segmentation and Classification of Skin Cancer Melanoma from Skin Lesion Images"], "weko_shared_id": -1}