New Methodology of Computer Aided Diagnostic System on Breast Cancer
In this paper, a new approach using ANFIS as a diagnosis system on WBCD problem is proposed. The automatic diagnosis of breast cancer is an important, real-world medical problem. It is occasionally difficult to attain the ultimate diagnosis even for medical experts due to the complexity and non-line...
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| creator | Song, HeeJun Lee, SeonGu Kim, Dongwon Park, GwiTae |
| description | In this paper, a new approach using ANFIS as a diagnosis system on WBCD problem is proposed. The automatic diagnosis of breast cancer is an important, real-world medical problem. It is occasionally difficult to attain the ultimate diagnosis even for medical experts due to the complexity and non-linearity of the relationships between the large measured factors. It is possibly resolved with using AI algorithms. ANFIS is an AI algorithm which has the advantages of both fuzzy inference system and neural networks. Therefore, it can deal with ambiguous data and learn from the past data. Applying ANFIS as a diagnostic system was considered in our experiment. In addition, the computational performance of diagnosis system is an important issue as well as the output correctness of the inference system. Methods of using recommended inputs generated by the Genetic-Algorithm, Decision-Tree and Correlation-Coefficient computation with ANFIS was proposed to reduce the computational overhead. |
| doi_str_mv | 10.1007/11427469_124 |
| format | Conference Proceeding |
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The automatic diagnosis of breast cancer is an important, real-world medical problem. It is occasionally difficult to attain the ultimate diagnosis even for medical experts due to the complexity and non-linearity of the relationships between the large measured factors. It is possibly resolved with using AI algorithms. ANFIS is an AI algorithm which has the advantages of both fuzzy inference system and neural networks. Therefore, it can deal with ambiguous data and learn from the past data. Applying ANFIS as a diagnostic system was considered in our experiment. In addition, the computational performance of diagnosis system is an important issue as well as the output correctness of the inference system. Methods of using recommended inputs generated by the Genetic-Algorithm, Decision-Tree and Correlation-Coefficient computation with ANFIS was proposed to reduce the computational overhead.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 3540259147</identifier><identifier>ISBN: 9783540259145</identifier><identifier>ISBN: 9783540259121</identifier><identifier>ISBN: 3540259120</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 3540320695</identifier><identifier>EISBN: 9783540320692</identifier><identifier>DOI: 10.1007/11427469_124</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Diagnosis System ; Exact sciences and technology ; Fuzzy Inference System ; Genetic Algorithm ; Inference System ; Learning and adaptive systems ; Ultimate Diagnosis</subject><ispartof>Advances in Neural Networks – ISNN 2005, 2005, p.780-789</ispartof><rights>Springer-Verlag Berlin Heidelberg 2005</rights><rights>2005 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/11427469_124$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/11427469_124$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>309,310,775,776,780,785,786,789,4035,4036,27904,38234,41421,42490</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=16882624$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><contributor>Yi, Zhang</contributor><contributor>Liao, Xiao-Feng</contributor><contributor>Wang, Jun</contributor><creatorcontrib>Song, HeeJun</creatorcontrib><creatorcontrib>Lee, SeonGu</creatorcontrib><creatorcontrib>Kim, Dongwon</creatorcontrib><creatorcontrib>Park, GwiTae</creatorcontrib><title>New Methodology of Computer Aided Diagnostic System on Breast Cancer</title><title>Advances in Neural Networks – ISNN 2005</title><description>In this paper, a new approach using ANFIS as a diagnosis system on WBCD problem is proposed. The automatic diagnosis of breast cancer is an important, real-world medical problem. It is occasionally difficult to attain the ultimate diagnosis even for medical experts due to the complexity and non-linearity of the relationships between the large measured factors. It is possibly resolved with using AI algorithms. ANFIS is an AI algorithm which has the advantages of both fuzzy inference system and neural networks. Therefore, it can deal with ambiguous data and learn from the past data. Applying ANFIS as a diagnostic system was considered in our experiment. In addition, the computational performance of diagnosis system is an important issue as well as the output correctness of the inference system. Methods of using recommended inputs generated by the Genetic-Algorithm, Decision-Tree and Correlation-Coefficient computation with ANFIS was proposed to reduce the computational overhead.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Diagnosis System</subject><subject>Exact sciences and technology</subject><subject>Fuzzy Inference System</subject><subject>Genetic Algorithm</subject><subject>Inference System</subject><subject>Learning and adaptive systems</subject><subject>Ultimate Diagnosis</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>3540259147</isbn><isbn>9783540259145</isbn><isbn>9783540259121</isbn><isbn>3540259120</isbn><isbn>3540320695</isbn><isbn>9783540320692</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpNUMtOwzAQNC-JUnrjA3zhghTY9SvxEVJeUoEDcLZM7JRAG0d2EOrfY1QO7GWkmdGsZgg5QThHgPICUbBSKG2QiR1yxKUAzkBpuUsmqBALzoXe2wpMahTlPpkAB1boUvBDMkvpA_JxVJmdkPmj_6YPfnwPLqzCckNDS-uwHr5GH-ll57yj884u-5DGrqHPmzT6NQ09vYreppHWtm98PCYHrV0lP_vDKXm9uX6p74rF0-19fbkoBsZgLBAbp5Bx1NKCcm3bVrJ1njuQpWhKl3ngJUifu1mtZeWEd1DxirNGVV7zKTnd5g42NXbVxvy9S2aI3drGjUFVVUwxkX1nW1_KUr_00byF8JkMgvkd0fwfkf8AIjxdCA</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Song, HeeJun</creator><creator>Lee, SeonGu</creator><creator>Kim, Dongwon</creator><creator>Park, GwiTae</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2005</creationdate><title>New Methodology of Computer Aided Diagnostic System on Breast Cancer</title><author>Song, HeeJun ; Lee, SeonGu ; Kim, Dongwon ; Park, GwiTae</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p220t-11cd6123195a06dfff85fde3d0574c7d19503705e124a9958d4ed083832c68e93</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Diagnosis System</topic><topic>Exact sciences and technology</topic><topic>Fuzzy Inference System</topic><topic>Genetic Algorithm</topic><topic>Inference System</topic><topic>Learning and adaptive systems</topic><topic>Ultimate Diagnosis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Song, HeeJun</creatorcontrib><creatorcontrib>Lee, SeonGu</creatorcontrib><creatorcontrib>Kim, Dongwon</creatorcontrib><creatorcontrib>Park, GwiTae</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Song, HeeJun</au><au>Lee, SeonGu</au><au>Kim, Dongwon</au><au>Park, GwiTae</au><au>Yi, Zhang</au><au>Liao, Xiao-Feng</au><au>Wang, Jun</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>New Methodology of Computer Aided Diagnostic System on Breast Cancer</atitle><btitle>Advances in Neural Networks – ISNN 2005</btitle><date>2005</date><risdate>2005</risdate><spage>780</spage><epage>789</epage><pages>780-789</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>3540259147</isbn><isbn>9783540259145</isbn><isbn>9783540259121</isbn><isbn>3540259120</isbn><eisbn>3540320695</eisbn><eisbn>9783540320692</eisbn><abstract>In this paper, a new approach using ANFIS as a diagnosis system on WBCD problem is proposed. The automatic diagnosis of breast cancer is an important, real-world medical problem. It is occasionally difficult to attain the ultimate diagnosis even for medical experts due to the complexity and non-linearity of the relationships between the large measured factors. It is possibly resolved with using AI algorithms. ANFIS is an AI algorithm which has the advantages of both fuzzy inference system and neural networks. Therefore, it can deal with ambiguous data and learn from the past data. Applying ANFIS as a diagnostic system was considered in our experiment. In addition, the computational performance of diagnosis system is an important issue as well as the output correctness of the inference system. Methods of using recommended inputs generated by the Genetic-Algorithm, Decision-Tree and Correlation-Coefficient computation with ANFIS was proposed to reduce the computational overhead.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11427469_124</doi><tpages>10</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Advances in Neural Networks – ISNN 2005, 2005, p.780-789 |
| issn | 0302-9743 1611-3349 |
| language | eng |
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| source | Springer Books |
| subjects | Applied sciences Artificial intelligence Computer science control theory systems Diagnosis System Exact sciences and technology Fuzzy Inference System Genetic Algorithm Inference System Learning and adaptive systems Ultimate Diagnosis |
| title | New Methodology of Computer Aided Diagnostic System on Breast Cancer |
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