Titlebook: Computational Intelligence Processing in Medical Diagnosis; Manfred Schmitt,Horia-Nicolai Teodorescu,Lakhmi C. Book 2002 Springer-Verlag B

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Generalizations and Extensions,osis greater than 50% formed the dichotomous supervisory variable for the neural network. The network contained 19 and 30 elements in the input and middle layers respectively. A single output element corresponded to the supervisory variable. Patient records were ordered according to the date of the

外来

Limit Theorems for Stopped Random Walks,ethodology. This chapter describes a modular neural network system which is being used for the detection and classification of breast cancer nuclei named Biopsy Analysis Support System (BASS). The system is based on a modular architecture where the detection and classification stages are independent

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Generalizations and Extensions, CAD can improve the accuracy of breast cancer detection and characterization by radiologists on mammograms. In this chapter, we discuss an important step — feature selection — in classifier design for CAD algorithms. Feature selection reduces the dimensionality of an available feature space and is

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https://doi.org/10.1007/978-0-387-87835-5In this chapter, we advocate the use of Computational Intelligence (CI) in diagnosis, in the context of using artificial intelligence in medicine and in health management. The methodological advantages, economic benefits, main trends and perspectives of using CI in health care and management are discussed.

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Renewal Processes and Random Walks,In intensive care units physicians are aware of a high lethality rate of septic shock patients. In this contribution we present typical problems and results of a retrospective, data driven analysis based on two neural network methods applied on the data of two clinical studies.

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Septic Shock Diagnosis by Neural Networks and Rule Based Systems,In intensive care units physicians are aware of a high lethality rate of septic shock patients. In this contribution we present typical problems and results of a retrospective, data driven analysis based on two neural network methods applied on the data of two clinical studies.

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