Artificial Neural Networks in Biomedicine

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Springer Science & Business Media, Feb 2, 2000 - Computers - 287 pages
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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.
 

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Contents

The Bayesian Paradigm Second Generation Neural Computing
11
12 Theory
12
121 Bayesian Learning
13
122 The Evidence Framework
15
1221 Error bars
16
1223 Regularisation
17
123 Committees
18
13 Example Results
20
115 Conclusion
149
Signal Processing
151
Independent Components Analysis
153
1221 The Decorrelating Manifold
155
1222 The Choice of NonLinearity
156
1223 ModelOrder Estimation
158
1231 Illustration
161
124 Applications
163

14 Conclusion
21
The Role of the Artificial Neural Network in the Characterisation of Complex Systems and the Prediction of Disease
25
21 Introduction
26
22 Diagnosis of Disease
28
23 Outcome Prediction
30
24 Conclusion
31
Genetic Evolution of Neural Network Architectures
39
The BiasVariance Problem
40
33 Genetic Algorithms and Artificial Neural Networks
41
331 Description of a General Method for Evolving ANN Architecture EANN
42
332 Prediction of Depression After Mania
43
334 ANN and the StabilitySpecialisation Choice
45
34 Conclusion
46
Computer Aided Diagnosis
49
The Application of PAPNET to Diagnostic Cytology
51
42 First Efforts at Automation in Cytology
52
43 Neural Networks
53
441 Components of the PAPNET System
54
4411 Technical factors affecting the performance of the machine
59
443 Application of the PAPNET System to Smears of Sputum
61
445 Application of the PAPNET System to Oesophageal Smears
62
45 Comment
63
ProstAsure Index A SerumBased Neural NetworkDerived Composite Index for Early Detection of Prostate Cancer
69
52 Clinical Background of Prostate Cancer and Derivation of the ProstAsure Index Algorithm
70
53 Validation of PI with Independent Clinical Data
72
54 Issues in Developing PI
73
55 Conclusion
76
Neurometric Assessment of Adequacy of Intraoperative Anaesthetic
81
62 Measuring Sensory Perception
82
64 Results
83
65 Implementation
86
66 Clinical Deployment
88
67 Healthcare Benefit
89
Classifying Spinal Measurements Using a Radial Basis Function Network
93
72 Data
94
723 Preprocessing the Data
95
73 Radial Basis Functions and Networks
96
74 Matrix Notation
97
75 Training RBF Networks
98
7522 Calculating the regularisation coefficients and the weights
99
7523 Forward subset selection of RBFs
101
7524 Input feature selection
102
77 Conclusion
103
GEORGIA An Overview
105
81 Introduction
106
82 The Medical Decision Support System
107
83 Learning Pattern Generation
109
84 Software and Hardware Implementation
110
85 ReTraining and ReConfiguring the MDSS
111
87 Conclusion
114
Patient Monitoring Using an Artificial Neural Network
117
92 Basic Statistical Appraisal of Vital Function Data
118
93 Neural Network Details
120
931 Default Training
121
94 Implementation
123
95 Clinical Trials
124
96 Clinical Practice
125
Benchmark of Approaches to Sequential Diagnosis
129
102 Preliminaries
130
103 Methods
132
103 12 The diagnostic algorithm for second order markov chains the Markov II algorithm
133
1032 The Fuzzy Methods
135
10325 The reduced algorithm with secondorder context fuzzy 2B
136
104 A Practical Example Comparative Analysis of Methods
137
105 Conclusion
138
Application of Neural Networks in the Diagnosis Of Pathological Speech
141
112 The Research Material and the Problems Considered
142
1122 Maxillofacial Surgery
143
1123 Orthodontics
144
113 The Signal Parameterisation
145
114 The Application of the Neural Networks and the Results
147
1241 Source Separation
164
125 Conclusion
166
Rest EEG Hidden Dynamics as a Discriminant for Brain Tumour Classification
169
131 Introduction
170
132 Characterising Hidden Dynamics
171
133 The Clinical Study
174
134 The Minimum Markov Order
176
135 Conclusion
179
Artificial Neural Network Control on Functional Electrical Stimulation Assisted Gait for Persons with Spinal Cord Injury
181
141 Introduction
182
142 Methods
183
143 Results
187
144 Discussion
191
The Application of Neural Networks to Interpret Evoked Potential Waveforms
195
152 The Medical Conditions Studied
196
154 The Relationship Between the CNV and the Medical Conditions
197
155 Experimental Procedures
198
157 Feature Extraction
199
158 Normalisation
200
1592 The Probabilistic Simplified Fuzzy ARTMAP
204
1593 ANN Training and Accuracy
205
15933 Committees of ANNs
206
1511 Results
207
1513 Future Developments
208
Image Processing
211
Intelligent Decision Support Systems in the Cytodiagnosis of Breast Carcinoma
213
162 Previous Work on Decision Support in this Domain
215
1632 Input Variables
216
1633 Partitioning of the Data
217
165 Logistic Regression
218
166 Data Derived Decision Tree
219
167 MultiLayer Perceptron Neural Networks
220
168 Adaptive Resonance Theory Mapping ARTMAP Neural Networks
222
1683 Results from the Cascaded System
225
169 Assessment of the Different Decision Support Systems
226
A NeuralBased System for the Automatic Classification and FollowUp of Diabetic Retinopathies
233
172 The DRA System
235
173 Hybrid Module
237
174 Committee Algorithms
239
1741 New Selection Algorithms
240
17411 Greedy selection
241
1742 Sequential Cooperation
242
1743 Experimental Results
243
175 Related Work
245
177 Conclusion
246
Classification of Chromosomes A Comparative Study of Neural Network and Statistical Approaches
249
1812 Chromosome Classification
250
1813 Experimental Data
251
182 The Neural Network Classifier
252
1822 Network Topology and Training
253
1823 Incorporating NonBanding Features
254
183 Classification Performance
255
1832 Comparison with Statistical Classifiers
256
184 The Use of Context in Classification
258
1842 Applying the Constraint by a Network
259
1843 Results of Applying the Context Network
260
185 Conclusion and Discussion
261
1852 Training Set Size and Application of Context
262
1853 Biological Context
263
The Importance of Features and Primitives for MultidimensionalMultichannel Image Processing
267
192 The Image Data Level
269
194 Region Segmentation Quality and Training Phase
270
195 Validation of Image Segmentation
271
196 Segmentation Complexity and Quantitative Error Evaluation
273
197 Feature Description
275
198 Feature Selection
276
199 A Preliminary Overview of Application Results
278
1910 Conclusion
281
Index
283
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About the author (2000)

Ifeachor is Professor of Intelligent Electronic Systems and Director of the Centre for Communications, Networks and Information Systems at the University of Plymouth, UK.