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Neural Networks Based System for Cancer Diagnosis Support
Witold Jacak, Karin Pröll
Research Center Hagenberg
Bioinformatics
Research output
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Contribution to conference
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Abstract
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peer-review
Overview
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Dive into the research topics of 'Neural Networks Based System for Cancer Diagnosis Support'. Together they form a unique fingerprint.
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Keyphrases
Neural Network
100%
Cancer Diagnosis
100%
Tumor Marker
100%
Post-diagnostic Support
100%
Network-based Systems
100%
Blood Parameters
63%
Cancer Types
36%
Input Values
18%
Cancer Prediction
18%
Cancer Occurrence
18%
Output Value
18%
Parameter Vector
18%
Cancer Risk
18%
Pattern Recognition Neural Network
18%
Hidden Neurons
18%
Training Set
9%
High Risk
9%
System Use
9%
Artificial Neural Network
9%
Aggregation Behavior
9%
Prediction System
9%
Prediction Method
9%
Parallel Computing
9%
Heterogeneous Neural Network
9%
Breast Cancer
9%
Medical Data
9%
Prediction Task
9%
Parameter Values
9%
Diagnosis Prediction
9%
Feedforward Neural Network
9%
Value Parameters
9%
Blood Count
9%
TMC125
9%
Neural Network System
9%
Pattern Recognition Network
9%
Neural Network Training
9%
Cancer Disease
9%
Neural Network Group
9%
Linear Activation Function
9%
Working System
9%
Individual Networks
9%
Cancer Marker
9%
Perceptron Network
9%
Engineering
Pattern Recognition
100%
Feedforward
66%
C Network
66%
Hidden Neuron
66%
Parameter Vector
66%
Input Value
66%
Output Value
66%
Tasks
33%
Artificial Neural Network
33%
Medical Data
33%
Perceptron
33%
Activation Function
33%
Aggregation Method
33%
Neural Network Training
33%
Neural Network System
33%
Pharmacology, Toxicology and Pharmaceutical Science
Malignant Neoplasm
100%
Tumor Marker
80%
Disease
6%
Breast Cancer
6%
Carcinoembryonic Antigen
6%
Mathematics
Neural Network
100%
Pattern Recognition
20%
Parameter Vector
13%
Hidden Neuron
13%
Input Value
13%
Artificial Neural Network
6%
Training Set
6%
Missing Value
6%
Great Value
6%
Perceptron
6%
Computer Science
Neural Network
100%
Pattern Recognition
23%
Parameter Vector
15%
Artificial Neural Network
7%
Parameter Value
7%
Feed Forward Neural Networks
7%
Activation Function
7%
Feedforward Neural Network
7%
Recognition Network
7%
Neural Network Training
7%
Biochemistry, Genetics and Molecular Biology
Tumor Marker
100%
Pattern Recognition
25%
Carcinoembryonic Antigen
8%
Artificial Neural Network
8%
Perceptron
8%
Blood Cell Count
8%