TY - GEN
T1 - A comparative study of database reduction methods for case-based computer-aided detection systems
T2 - Medical Imaging 2009: Computer-Aided Diagnosis
AU - Mazurowski, Maciej A.
AU - Malof, Jordan M.
AU - Zurada, Jacek M.
AU - Tourassi, Georgia D.
PY - 2009
Y1 - 2009
N2 - In case-based computer-aided decision systems (CB-CAD) a query case is compared to known examples stored in the systems case base (also called a reference library). These systems offer competitive classification performance and are easy to expand. However, they also require efficient management of the case base. As CB-CAD systems are becoming more popular, the problem of case base optimization has recently attracted interest among CAD researchers. In this paper we present preliminary results of a study comparing several case base reduction techniques. We implemented six techniques previously proposed in machine learning literature and applied it to the classification problem of distinguishing masses and normal tissue in mammographic regions of interest. The results show that the random mutation hill climbing technique offers a drastic reduction of the number of case base examples while providing a significant improvement in classification performance. Random selection allowed for reduction of the case base to 30% without notable decrease in performance. The remaining techniques (i.e., condensed nearest neighbor, reduced nearest neighbor, edited nearest neighbor, and All k-NN) resulted in moderate reduction (to 50-70% of the original size) at the cost of decrease in CB-CAD performance.
AB - In case-based computer-aided decision systems (CB-CAD) a query case is compared to known examples stored in the systems case base (also called a reference library). These systems offer competitive classification performance and are easy to expand. However, they also require efficient management of the case base. As CB-CAD systems are becoming more popular, the problem of case base optimization has recently attracted interest among CAD researchers. In this paper we present preliminary results of a study comparing several case base reduction techniques. We implemented six techniques previously proposed in machine learning literature and applied it to the classification problem of distinguishing masses and normal tissue in mammographic regions of interest. The results show that the random mutation hill climbing technique offers a drastic reduction of the number of case base examples while providing a significant improvement in classification performance. Random selection allowed for reduction of the case base to 30% without notable decrease in performance. The remaining techniques (i.e., condensed nearest neighbor, reduced nearest neighbor, edited nearest neighbor, and All k-NN) resulted in moderate reduction (to 50-70% of the original size) at the cost of decrease in CB-CAD performance.
KW - Classification and classifier design
KW - Database construction
KW - Detection
KW - Mammography
UR - http://www.scopus.com/inward/record.url?scp=66749158645&partnerID=8YFLogxK
U2 - 10.1117/12.812442
DO - 10.1117/12.812442
M3 - Conference contribution
AN - SCOPUS:66749158645
SN - 9780819475114
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2009
Y2 - 10 February 2009 through 12 February 2009
ER -