{"created":"2020-09-01T09:51:20.412175+00:00","id":3260,"links":{},"metadata":{"_buckets":{"deposit":"02c296b5-397d-4970-8d82-aa3f9e1bfa96"},"_deposit":{"id":"3260","owners":[],"pid":{"revision_id":0,"type":"recid","value":"3260"},"status":"published"},"_oai":{"id":"oai:meral.edu.mm:recid/3260","sets":["1582963302567:1597824273898"]},"communities":["ucsy"],"item_1583103067471":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"Classification of Breast Cancer Using Radial Basic Function Neural Network","subitem_1551255648112":"en"}]},"item_1583103085720":{"attribute_name":"Description","attribute_value_mlt":[{"interim":"Breast cancer is the second most commonform of cancer among females and also the fifthmost cause of cancer deaths worldwide. The earlydetection is the best form of cure and hence timelyand accurate diagnosis of the tumor is extremelyvital. The use of learning machine and artificialintelligence techniques has revolutionized theprocess of diagnosis of the breast cancer. In thissystem Radial Basic Function Neural Networkwith Gaussian Function in hidden layer is used toclassify the Breast Cancer. There are 327 recordsto implement the system. In each record includes12 attributes. This system consists of three phases:preprocessing phase, training phase, testingphase.In preprocessing step, convert the inputdata into the binary number. In training phase, theRBF neural network is used to train the inputvectors, symptoms of breast cancer. Twelveattributes of training datasets are presented intothe input layer of the neural network. The RBFneural network is trained with training data andsave the optimal parameters. In testing phase, thetesting data inputsinto the trained neural networkwith optimal parameters.The system displays oneof five classes of breast cancer stages."}]},"item_1583103108160":{"attribute_name":"Keywords","attribute_value":[]},"item_1583103120197":{"attribute_name":"Files","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2019-07-19"}],"displaytype":"preview","filename":"159_PDFsam_PSC_final proof.pdf","filesize":[{"value":"137 Kb"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"url":"https://meral.edu.mm/record/3260/files/159_PDFsam_PSC_final proof.pdf"},"version_id":"745e6b81-1507-4590-9d9b-f0290447452d"}]},"item_1583103131163":{"attribute_name":"Journal articles","attribute_value_mlt":[{"subitem_issue":"","subitem_journal_title":"Eighth Local Conference on Parallel and Soft Computing","subitem_pages":"","subitem_volume":""}]},"item_1583103147082":{"attribute_name":"Conference papers","attribute_value_mlt":[{"subitem_acronym":"","subitem_c_date":"","subitem_conference_title":"","subitem_part":"","subitem_place":"","subitem_session":"","subitem_website":""}]},"item_1583103211336":{"attribute_name":"Books/reports/chapters","attribute_value_mlt":[{"subitem_book_title":"","subitem_isbn":"","subitem_pages":"","subitem_place":"","subitem_publisher":""}]},"item_1583103233624":{"attribute_name":"Thesis/dissertations","attribute_value_mlt":[{"subitem_awarding_university":"","subitem_supervisor(s)":[{"subitem_supervisor":""}]}]},"item_1583105942107":{"attribute_name":"Authors","attribute_value_mlt":[{"subitem_authors":[{"subitem_authors_fullname":"Oo, Khin Mar Lar"}]}]},"item_1583108359239":{"attribute_name":"Upload type","attribute_value_mlt":[{"interim":"Publication"}]},"item_1583108428133":{"attribute_name":"Publication type","attribute_value_mlt":[{"interim":"Article"}]},"item_1583159729339":{"attribute_name":"Publication date","attribute_value":"2017-12-27"},"item_1583159847033":{"attribute_name":"Identifier","attribute_value":"http://onlineresource.ucsy.edu.mm/handle/123456789/1028"},"item_title":"Classification of Breast Cancer Using Radial Basic Function Neural Network","item_type_id":"21","owner":"1","path":["1597824273898"],"publish_date":"2019-07-19","publish_status":"0","recid":"3260","relation_version_is_last":true,"title":["Classification of Breast Cancer Using Radial Basic Function Neural Network"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2021-12-13T00:41:51.207141+00:00"}