cqu_4357+ATTACHMENT07+ATTACHMENT07.4.pdf (110.79 kB)
Impact of soft clustering on classification of suspicious areas in digital mammograms
conference contribution
posted on 2017-12-06, 00:00 authored by NULL McLeodNULL McLeod, Brijesh VermaThis paper investigates a soft cluster based approach for determining the impact of soft clustering on the training of a neural network classifier for the classification of suspicious areas in digital mammograms. An approach is proposed that first creates soft clusters for each available class and then uses soft clusters to form subclasses within benign and malignant classes. The incorporation of soft clusters in the classification process is designed to increase the learning abilities and improve the accuracy of the classification system. The experiments using soft clusters based proposed approach and a standard neural network classifier have been conducted on a benchmark database. The results have been analysed and presented in this paper.
Funding
Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)
History
Start Page
109End Page
114Number of Pages
6Start Date
2008-01-01Finish Date
2008-01-01ISBN-13
9781424429578Location
Sydney, AustraliaPublisher
IEEEPlace of Publication
USAFull Text URL
Peer Reviewed
- Yes
Open Access
- No
External Author Affiliations
Faculty of Business and Informatics;Era Eligible
- Yes