Identification of masses in digital mammograms with MLP and RBF Nets

Copyright © 2000 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.IEEE-INNS-ENNS International Joint Conference on Neural Networks 2000 (IJCNN 2000), Como, Italy, 24-27 July 2000We study the identification of masses in digital mammograms using texture analysis. A number of texture measures are calculated for bilateral difference images showing regions of interest. The measurements are made on co-occurrence matrices in four different direction giving a total of seventy features. These features include the ones proposed by Haralick et al. (1973) and Chan et al. (1997). We study a total of 144 breast images from the MIAS database. The dimensionality of the dataset is reduced using principal components analysis (PCA), PCA components are classified using both multilayer perceptron networks using backpropagation (MLP) and radial basis functions based on Gaussian kernels (RBF). The two methods are compared on the same data across a ten fold cross-validation. The results are generated on the average recognition rate over these folds on correctly recognising masses and normal regions. Further analysis is based on the receiver operating characteristic (ROC) plots. The best results show recognition rates of 77% correct recognition and an area under the ROC curve value Az of 0.7

Data Assimilation Enhancements to Air Force Weathers Land Information System

The United States Air Force (USAF) has a proud and storied tradition of enabling significant advancements in the area of characterizing and modeling land state information. 557th Weather Wing (557 WW; DoDs Executive Agent for Land Information) provides routine geospatial intelligence information to warfighters, planners, and decision makers at all echelons and services of the U.S. military, government and intelligence community. 557 WW and its predecessors have been home to the DoDs only operational regional and global land data analysis systems since January 1958. As a trusted partner since 2005, Air Force Weather (AFW) has relied on the Hydrological Sciences Laboratory at NASA/GSFC to lead the interagency scientific collaboration known as the Land Information System (LIS). LIS is an advanced software framework for high performance land surface modeling and data assimilation of geospatial intelligence (GEOINT) information

An adaptive knowledge-based model for detecting masses in screening mammograms

EThOS - Electronic Theses Online ServiceGBUnited Kingdo