Name
Abstract | ||
|---|---|---|
Computational diagnosis tools are becoming indispensable to support modern medical diagnosis. This research work introduces an hybrid soft computing scheme consisting of Fuzzy Cognitive Maps and the effective Active Hebbian Learning (AHL) algorithm for tumor characterization. The proposed method exploits human experts' knowledge on histopathology expressed in descriptive terms and concepts and it is enhanced with Hebbian learning and then it classifies tumors based on the morphology of tissues. This method was validated in clinical data and the results enforce the effectiveness of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-24844-6_161 | Lecture Notes in Artificial Intelligence |
Keywords | Field | DocType |
soft computing,fuzzy cognitive map,hebbian learning,medical diagnosis | Cognitive map,Computer science,Fuzzy cognitive map,Fuzzy logic,Hebbian theory,Artificial intelligence,Soft computing,Complete information,Machine learning,Medical diagnosis | Conference |
Volume | ISSN | Citations |
3070 | 0302-9743 | 2 |
PageRank | References | Authors |
0.43 | 5 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elpiniki Papageorgiou | 1 | 269 | 18.18 |
| Panagiota Spyridonos | 2 | 222 | 17.43 |
| Chrysostomos D. Stylios | 3 | 649 | 52.33 |
| Panagiota Ravazoula | 4 | 152 | 12.25 |
| George Nikiforidis | 5 | 225 | 21.70 |
| Peter P. Groumpos | 6 | 493 | 36.61 |