Overview

The main aim of this study is to present a sample of research on the application of novel artificial intelligence paradigms to the diagnosis and prognosis of breast cancer. These paradigms include neural networks, fuzzy logic and evolutionary computing. Artificial intelligence techniques offer advantages - such as adaptation, fault tolerance, learning and human-like behaviour - over conventional computing techniques. The idea is to combine the pathological, intelligent and statistical approaches to enable simple and accurate diagnosis and prognosis. The book contains contributions from Australia, Germany, Italy and the USA.

Full Product Details

Author:   Lakhmi C Jain (Univ Of South Australia, Australia) ,  Ashlesha Jain (Univ Of Adelaide, Australia) ,  Ajita Jain (Univ Of South Australia, Australia) ,  Sandhya Jain (Julia Farr Centre, Australia)
Publisher:   World Scientific Publishing Co Pte Ltd
Imprint:   World Scientific Publishing Co Pte Ltd
Volume:   39
Dimensions:   Width: 16.20cm , Height: 2.10cm , Length: 23.00cm
Weight:   0.517kg
ISBN:  

9789810243746

ISBN 10:   981024374
Pages:   348
Publication Date:   23 August 2000
Audience:   College/higher education ,  Professional and scholarly ,  Postgraduate, Research & Scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Out of stock  
The supplier is temporarily out of stock of this item. It will be ordered for you on backorder and shipped when it becomes available.

Table of Contents

An introduction to breast cancer diagnosis, prognosis, and artificial intelligence, N. Harbeck et al; automatic image feature extraction for diagnosis and prognosis of breast cancer, M.J. Bottema et al; decision support in breast cancer - recent advances in prognostic and predictive techniques, R. Kates et al; MammoNet - a Bayesian network diagnosing breast cancer, L.M. Roberts; predicting prognosis and treatment response in breast cancer patients, M.G. Daidone and D. Coradini; computer-aided breast cancer diagnosis, H.-P. Chan et al; which decision support technologies are appropriate for the cytodiagnosis of breast cancer? S.S. Cross et al; Xcyt - a system for remote cytological diagnosis and prognosis of breast cancer, W.N. Street.

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