Skip to main content
SpringerLink
Log in

Artificial Neural Networks in Biomedicine

  • Book
  • © 2000

Overview

Editors:
  1. Paulo J. G. Lisboa

    1. School of Computing and Mathematical Sciences, Liverpool John Moore’s University, Liverpool, UK

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Emmanuel C. Ifeachor

    1. School of Electronic, Communication and Electrical Engineering, University of Plymouth, Plymouth, UK

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  3. Piotr S. Szczepaniak

    1. Institute of Computer Science, Technical University of Lodz, Lodz, Poland

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Perspectives in Neural Computing (PERSPECT.NEURAL)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (20 chapters)

  1. Front Matter

    Pages i-xiv
    Download chapter PDF

  2. Introduction

    1. Introduction

      • Paulo J. G. Lisboa, Emmanuel C. Ifeachor, Piotr S. Szczepaniak
      Pages 1-7

  3. Tutorial and Review

    1. Front Matter

      Pages 9-10
      Download chapter PDF

    2. The Bayesian Paradigm: Second Generation Neural Computing

      • William D. Penny, Dirk Husmeier, Stephen J. Roberts
      Pages 11-23

    3. The Role of the Artificial Neural Network in the Characterisation of Complex Systems and the Prediction of Disease

      • William G. Baxt
      Pages 25-37

    4. Genetic Evolution of Neural Network Architectures

      • M. F. Jefferson, N. Pendleton, S. B. Lucas
      Pages 39-48

  4. Computer Aided Diagnosis

    1. Front Matter

      Pages 49-50
      Download chapter PDF

    2. The Application of PAPNET to Diagnostic Cytology

      • Leopold G. Koss
      Pages 51-67

    3. ProstAsure Index — A Serum-Based Neural Network-Derived Composite Index for Early Detection of Prostate Cancer

      • Zhen Zhang, Hong Zhang
      Pages 69-79

    4. Neurometric Assessment of Adequacy of Intraoperative Anaesthetic

      • Lars J. Kangas, Paul E. Keller
      Pages 81-91

    5. Classifying Spinal Measurements Using a Radial Basis Function Network

      • S. J. Langdell, J. C. Mason
      Pages 93-104

    6. Georgia: An Overview

      • George - Peter K. Economou
      Pages 105-115

    7. Patient Monitoring Using an Artificial Neural Network

      • Nigel Dodd
      Pages 117-128

    8. Benchmark of Approaches to Sequential Diagnosis

      • Marek W. Kurzyński
      Pages 129-140

    9. Application Of Neural Networks in the Diagnosis of Pathological Speech

      • Ryszard Tadeusiewicz
      Pages 141-150

  5. Signal Processing

    1. Front Matter

      Pages 151-152
      Download chapter PDF

    2. Independent Components Analysis

      • Richard Everson, Stephen J. Roberts
      Pages 153-168

    3. Rest EEG Hidden Dynamics as a Discriminant for Brain Tumour Classification

      • Rosaria Silipo, Gustavo Deco, Helmut Bartsch
      Pages 169-180

    4. Artificial Neural Network Control on Functional Electrical Stimulation Assisted Gait for Persons with Spinal Cord Injury

      • Kai-yu Tong, Malcolm H. Granat
      Pages 181-193

    5. The Application of Neural Networks to Interpret Evoked Potential Waveforms

      • Barrie W. Jervis
      Pages 195-210
Back to top

Keywords

About this book

Following the intense research activIties of the last decade, artificial neural networks have emerged as one of the most promising new technologies for improving the quality of healthcare. Many successful applications of neural networks to biomedical problems have been reported which demonstrate, convincingly, the distinct benefits of neural networks, although many ofthese have only undergone a limited clinical evaluation. Healthcare providers and developers alike have discovered that medicine and healthcare are fertile areas for neural networks: the problems here require expertise and often involve non-trivial pattern recognition tasks - there are genuine difficulties with conventional methods, and data can be plentiful. The intense research activities in medical neural networks, and allied areas of artificial intelligence, have led to a substantial body of knowledge and the introduction 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.

Editors and Affiliations

  • School of Computing and Mathematical Sciences, Liverpool John Moore’s University, Liverpool, UK

    Paulo J. G. Lisboa

  • School of Electronic, Communication and Electrical Engineering, University of Plymouth, Plymouth, UK

    Emmanuel C. Ifeachor

  • Institute of Computer Science, Technical University of Lodz, Lodz, Poland

    Piotr S. Szczepaniak

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation