Evolving Spiking Neural Network Topologies for Breast Cancer Classification in a Dielectrically Heterogeneous Breast
暂无分享,去创建一个
Edward Jones | Fearghal Morgan | Brian McGinley | Seamus Cawley | Martin Glavin | Martin O'Halloran | Raquel C. Conceicao | E. Jones | M. Glavin | Brian McGinley | F. Morgan | R. Conceição | S. Cawley | M. O’halloran
[1] Karri Muinonen,et al. Introducing the Gaussian shape hypothesis for asteroids and comets , 1998 .
[2] R.M. Rangayyan,et al. Shape Analysis of Breast Masses in Mammograms via the Fractal Dimension , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.
[3] Barry D. Van Veen,et al. Breast Tumor Characterization Based on Ultrawideband Microwave Backscatter , 2008, IEEE Transactions on Biomedical Engineering.
[4] Larry D. Travis,et al. Light scattering by nonspherical particles : theory, measurements, and applications , 1998 .
[5] D. Land,et al. Dielectric properties of female human breast tissue measured in vitro at 3.2 GHz. , 1992, Physics in medicine and biology.
[6] S. S. Chaudhary,et al. Dielectric properties of normal & malignant human breast tissues at radiowave & microwave frequencies. , 1984, Indian journal of biochemistry & biophysics.
[7] Edward Jones,et al. Support Vector Machines for the Classification of Early-Stage Breast Cancer Based on Radar Target Signatures , 2010 .
[8] Fearghal Morgan,et al. Investigating the Suitability of FPAAs for Evolved Hardware Spiking Neural Networks , 2008, ICES.
[9] Edward Jones,et al. Investigation of Classifiers for Early-Stage Breast Cancer Based on Radar Target Signatures , 2010 .
[10] Raja Syamsul Azmir Raja Abdullah,et al. 3D experimental detection and discrimination of malignant and benign breast tumor using NN-based UWB imaging system , 2011 .
[11] M. Lindstrom,et al. A large-scale study of the ultrawideband microwave dielectric properties of normal breast tissue obtained from reduction surgeries , 2007, Physics in medicine and biology.
[12] Sharyl J. Nass,et al. Mammography and Beyond: Developing Technologies for the Early Detection of Breast Cancer , 2001 .
[13] M. O’Halloran,et al. Spiking Neural Networks for Breast Cancer Classification in a Dielectrically Heterogeneous Breast , 2011 .
[14] W. Joines,et al. The measured electrical properties of normal and malignant human tissues from 50 to 900 MHz. , 1994, Medical physics.
[15] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .
[16] M. O’Halloran,et al. Evaluation of Features and Classifiers for Classification of Early-stage Breast Cancer , 2011 .
[17] Stuchly,et al. Dielectric properties of breast carcinoma and the surrounding tissues , 1988, IEEE Transactions on Biomedical Engineering.
[18] David E. Goldberg,et al. Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.
[19] Edward Jones,et al. EFFECTS OF DIELECTRIC HETEROGENEITY IN THE PERFORMANCE OF BREAST TUMOUR CLASSIFIERS , 2011 .
[20] Wofgang Maas,et al. Networks of spiking neurons: the third generation of neural network models , 1997 .
[21] Liam McDaid,et al. EMBRACE-SysC for analysis of NoC-based Spiking Neural Network architectures , 2010, 2010 International Symposium on System on Chip.
[22] Karri Muinonen,et al. Light Scattering by Stochastically Shaped Particles , 2000 .
[23] Risto Miikkulainen,et al. Evolving Neural Networks through Augmenting Topologies , 2002, Evolutionary Computation.
[24] J. Dixon,et al. ABC of breast diseases , 2012 .
[25] Wolfgang Maass,et al. Computing with spiking neurons , 1999 .
[26] M. Lindstrom,et al. A large-scale study of the ultrawideband microwave dielectric properties of normal, benign and malignant breast tissues obtained from cancer surgeries , 2007, Physics in medicine and biology.