Name
Abstract | ||
|---|---|---|
Breast cancer screening has reference to screening of asymptomatic, generally healthy women for breast cancer, to identify those who should receive a follow up check. Early screening can detect non-invasive ductal carcinoma in situ (called “pre breast cancer”), which almost never forms a lump and is generally non-detectible, except by mammography. This paper will describe the design and preliminary evaluation of this PNN/GRNN ensemble pre-screener, in the context of a possible pre-screening protocol, which may, if required, include other data. The results show that using the ensemble technique provides almost a 20% AUC increase over the average standalone PNN and almost 10% over the best performing PNN. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.procs.2012.09.101 | Procedia Computer Science |
Keywords | Field | DocType |
Bioinformatics ,Biomedical ,Statistical neural networks ,Breast cancer screening | Asymptomatic,Mammography,Ductal carcinoma,Breast cancer,Computer science,Breast cancer screening,Processor design,Artificial intelligence,Machine learning | Journal |
Volume | ISSN | Citations |
12 | 1877-0509 | 1 |
PageRank | References | Authors |
0.37 | 1 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Walker H. Land Jr. | 1 | 50 | 11.04 |
| Xinpei Ma | 2 | 3 | 0.95 |
| Erin Barnes | 3 | 1 | 1.05 |
| Xingye Qiao | 4 | 23 | 6.81 |
| John J. Heine | 5 | 29 | 6.07 |
| Timothy Masters | 6 | 1 | 0.37 |
| Jin-woo Park | 7 | 337 | 59.76 |