| pubmed-article:9419649 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:9419649 | lifeskim:mentions | umls-concept:C0006142 | lld:lifeskim |
| pubmed-article:9419649 | lifeskim:mentions | umls-concept:C0598941 | lld:lifeskim |
| pubmed-article:9419649 | lifeskim:mentions | umls-concept:C0011905 | lld:lifeskim |
| pubmed-article:9419649 | lifeskim:mentions | umls-concept:C0449445 | lld:lifeskim |
| pubmed-article:9419649 | lifeskim:mentions | umls-concept:C2004457 | lld:lifeskim |
| pubmed-article:9419649 | lifeskim:mentions | umls-concept:C2348519 | lld:lifeskim |
| pubmed-article:9419649 | pubmed:issue | 10 | lld:pubmed |
| pubmed-article:9419649 | pubmed:dateCreated | 1998-2-6 | lld:pubmed |
| pubmed-article:9419649 | pubmed:abstractText | An artificial neural network (ANN) approach was developed for the computer-aided diagnosis of mammography using an optimally minimized number of input features. | lld:pubmed |
| pubmed-article:9419649 | pubmed:grant | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:9419649 | pubmed:language | eng | lld:pubmed |
| pubmed-article:9419649 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:9419649 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:9419649 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:9419649 | pubmed:month | Oct | lld:pubmed |
| pubmed-article:9419649 | pubmed:issn | 1076-6332 | lld:pubmed |
| pubmed-article:9419649 | pubmed:author | pubmed-author:BakerJ AJA | lld:pubmed |
| pubmed-article:9419649 | pubmed:author | pubmed-author:RoJ SJS | lld:pubmed |
| pubmed-article:9419649 | pubmed:author | pubmed-author:FloydC ECEJr | lld:pubmed |
| pubmed-article:9419649 | pubmed:author | pubmed-author:KornguthP JPJ | lld:pubmed |
| pubmed-article:9419649 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:9419649 | pubmed:volume | 2 | lld:pubmed |
| pubmed-article:9419649 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:9419649 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:9419649 | pubmed:pagination | 841-50 | lld:pubmed |
| pubmed-article:9419649 | pubmed:dateRevised | 2007-11-14 | lld:pubmed |
| pubmed-article:9419649 | pubmed:meshHeading | pubmed-meshheading:9419649-... | lld:pubmed |
| pubmed-article:9419649 | pubmed:meshHeading | pubmed-meshheading:9419649-... | lld:pubmed |
| pubmed-article:9419649 | pubmed:meshHeading | pubmed-meshheading:9419649-... | lld:pubmed |
| pubmed-article:9419649 | pubmed:meshHeading | pubmed-meshheading:9419649-... | lld:pubmed |
| pubmed-article:9419649 | pubmed:meshHeading | pubmed-meshheading:9419649-... | lld:pubmed |
| pubmed-article:9419649 | pubmed:meshHeading | pubmed-meshheading:9419649-... | lld:pubmed |
| pubmed-article:9419649 | pubmed:meshHeading | pubmed-meshheading:9419649-... | lld:pubmed |
| pubmed-article:9419649 | pubmed:year | 1995 | lld:pubmed |
| pubmed-article:9419649 | pubmed:articleTitle | Computer-aided diagnosis of breast cancer: artificial neural network approach for optimized merging of mammographic features. | lld:pubmed |
| pubmed-article:9419649 | pubmed:affiliation | Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA. | lld:pubmed |
| pubmed-article:9419649 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:9419649 | pubmed:publicationType | Comparative Study | lld:pubmed |
| pubmed-article:9419649 | pubmed:publicationType | Research Support, U.S. Gov't, P.H.S. | lld:pubmed |
| pubmed-article:9419649 | pubmed:publicationType | Research Support, U.S. Gov't, Non-P.H.S. | lld:pubmed |
| http://linkedlifedata.com/r... | pubmed:referesTo | pubmed-article:9419649 | lld:pubmed |
| http://linkedlifedata.com/r... | pubmed:referesTo | pubmed-article:9419649 | lld:pubmed |
