TY - GEN
T1 - Rough sets in medical informatics applications
AU - Hassanien, Aboul Ella
AU - Abraham, Ajith
AU - Peters, James F.
AU - Schaefer, Gerald
PY - 2009/1/1
Y1 - 2009/1/1
N2 - Rough sets offer an effective approach of managing uncertainties and can be employed for tasks such as data dependency analysis, feature identification, dimensionality reduction, and pattern classification. As these tasks are common in many medical applications it is only natural that rough sets, despite their relative 'youth' compared to other techniques, provide a suitable method in such applications. In this paper, we provide a short summary on the use of rough sets in the medical informatics domain, focussing on applications of medical image segmentation, pattern classification and computer assisted medical decision making.
AB - Rough sets offer an effective approach of managing uncertainties and can be employed for tasks such as data dependency analysis, feature identification, dimensionality reduction, and pattern classification. As these tasks are common in many medical applications it is only natural that rough sets, despite their relative 'youth' compared to other techniques, provide a suitable method in such applications. In this paper, we provide a short summary on the use of rough sets in the medical informatics domain, focussing on applications of medical image segmentation, pattern classification and computer assisted medical decision making.
UR - http://www.scopus.com/inward/record.url?scp=84885228263&partnerID=8YFLogxK
UR - https://link.springer.com/chapter/10.1007/978-3-540-89619-7_3
U2 - 10.1007/978-3-540-89619-7_3
DO - 10.1007/978-3-540-89619-7_3
M3 - Conference publication
AN - SCOPUS:84885228263
SN - 9783540896180
VL - 58
T3 - Advances in Intelligent and Soft Computing
SP - 23
EP - 30
BT - Advances in Intelligent and Soft Computing
PB - Springer
ER -