C.E. Floyd, J.Y. Lo, A.J. Yun, D.C. Sullivan, and P.J. Kornguth. Prediction of breast cancer malignancy using an artificial neural network. Cancer, 74:2944--2998, 1994. Home/Search Document Not in Database Summary Related Articles Check |
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An Evolutionary Artificial Neural Networks Approach for Breast.. - Abbass (2002) (1 citation) (Correct)
....Keywords Pareto optimization, di#erential evolution, artificial neural networks, breast cancer. 1 Introduction The economic and social values of Breast Cancer Diagnosis (BCD) are very high. As a result, the problem has attracted many researchers in the area of computational intelligence recently [6, 8, 10, 22, 26, 32, 33, 34]. Because of the importance of achieving highly accurate classification, Artificial Neural Networks (ANNs) are among the most common methods for BCD. Research in the area of using ANNs for medical purposes more specifically BCD [6, 8, 10, 22, 26, 32, 34] has been at the center of attention ....
....intelligence recently [6, 8, 10, 22, 26, 32, 33, 34] Because of the importance of achieving highly accurate classification, Artificial Neural Networks (ANNs) are among the most common methods for BCD. Research in the area of using ANNs for medical purposes more specifically BCD [6, 8, 10, 22, 26, 32, 34] has been at the center of attention for several years. Unfortunately, to our present knowledge, none of this type of research was able to enter the clinic either in terms of routine use or to replace the radiologist. This could be ascribed to a number of factors. The first problem was the ....
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C.E. Floyd, J.Y. Lo, A.J. Yun, D.C. Sullivan, and P.J. Kornguth. Prediction of breast cancer malignancy using an artificial neural network. Cancer, 74:2944--2998, 1994.
Detection, Synthesis and Compression in Mammographic Image.. - Clay Spence Lucas (2001) (1 citation) (Correct)
....In mammographic computer assisted diagnosis (CAD) one typically estimates , the conditional probability of class (e.g. benign vs. malignant) given image or a set of features extracted from . Previous efforts have concentrated on the development of such discriminant models for CAD [1][2] 3] 4] By contrast, a generative model, has many attractive features. Classification is possible by training a distribution for each class and using Bayes rule to obtain 16350 16350 13230 2 . However there are many other benefits of having a model of ....
C.E. Floyd, J.Y. Lo, A.J. Yun, D.C. Sullivan, and P.J. Kornguth, "Prediction of breast cancer malignancy using an artificial neural network," Cancer, vol. 74, pp. 2944--2948, 1994.
Decision Aids in Radiology - Kahn, Jr. (Correct)
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Floyd CE Jr, Lo JY, Yun AJ, et al: Prediction of breast cancer malignancy using an artificial neural network. Cancer 74:2944, 1994
On the Misuses of Artificial Neural Networks for.. - Schwarzer, Vach.. (2000) (1 citation) (Correct)
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Floyd Jr. CE, Lo JY, Yun AJ, Sullivan DC, Kornguth PJ. Prediction of breast cancer malignancy using an artificial neural network. Cancer. 1994; 74:2944-8.
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