Titlebook: Artificial Neural Networks in Biomedicine; Paulo J. G. Lisboa,Emmanuel C. Ifeachor,Piotr S. S Book 2000 Springer-Verlag London 2000 Elektr

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1431-6854 ntroduction of some neural systems into clinical practice. An aim of this book is to provide a coherent framework for some of the most experienced users and developers of medical neural networks in the world to share their knowledge and expertise with readers.978-1-85233-005-7978-1-4471-0487-2Series ISSN 1431-6854

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The Application of PAPNET to Diagnostic Cytologyn detail the microscopic features that are of diagnostic significance and the interested reader is referred to other sources [1],[2]. Suffice to say, that the differences between benign and malignant cells are reflected mainly in the configuration and staining qualities of the nucleus of the cells,

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Georgia: An Overviewns (LPs). These LPs can use incomplete data, independently boost their particular characteristics, and they do not grow into inhibiting numbers. GEORGIA resulted in a generalisation performance of 88%-95% correct classification of unknown medical inputs when first taught. Moreover, a `user-friendly’

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Paulo J. G. Lisboa,Emmanuel C. Ifeachor,Piotr S. S

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https://doi.org/10.1007/BFb0108599eural network to some more complicated human biomedical problems might be more successful than the traditional linear approaches used in the past. Described here is the progress that has been made in the application of the artificial neural network to the analysis of human disease. Covered herein is

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