Probabilistic Neural Networks Structure Optimization through Genetic Algorithm for Recognizing Faces under Illumination Variationss

2014 ◽  
Author(s):  
Benyamin Kusumoputro ◽  
Stephen Stephen ◽  
Lina Lina
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Structure Optimization ◽  
Probabilistic Neural Networks ◽  
Networks Structure

An intelligent decision support system for quantitative assessment of gastric atrophy

2011 ◽  
Vol 64 (4) ◽  
pp. 330-337 ◽  
Author(s):  
Faruq A Al-Omari ◽  
Ismail I Matalka ◽  
Mohammad A Al-Jarrah ◽  
Fatima N Obeidat ◽  
Faisal M Kanaan
Keyword(s):  
Neural Networks ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Imaging Techniques ◽  
Gastric Atrophy ◽  
Probabilistic Neural Networks ◽  
Sydney System ◽  
Updated Sydney System ◽  
Networks Structure

AimsTo build an automated decision support system to assist pathologists in grading gastric atrophy according to the updated Sydney system.MethodsA database of 143 biopsies was used to train and examine the proposed system. A panel of three experienced pathologists reached a consensus regarding the grading of the studied biopsies using the visual scale of the updated Sydney system. Digital imaging techniques were utilised to extract a set of discriminating morphological features that describe each atrophy grade sufficiently and uniquely. A probabilistic neural networks structure was used to build a grading system. To evaluate the performance of the proposed system, 66% of the biopsies (94 biopsy images) were used for training purposes and 34% (49 biopsy images) were used for testing and validation purposes.ResultsDuring the training phase, a 98.9% precision was achieved, whereas during testing, a precision of 95.9% was achieved. The overall precision achieved was 97.9%.ConclusionsA fully automated decision support system to grade gastric atrophy according to the updated Sydney system is proposed. The system utilises advanced image processing techniques and probabilistic neural networks in conducting the assessment. The proposed system eliminates inter- and intra-observer variations with high reproducibility.

A novel full structure optimization algorithm for radial basis probabilistic neural networks

2006 ◽  
Vol 70 (1-3) ◽  
pp. 592-596 ◽  
Author(s):  
Ji-Xiang Du ◽  
De-Shuang Huang ◽  
Guo-Jun Zhang ◽  
Zeng-Fu Wang
Keyword(s):  
Neural Networks ◽  
Optimization Algorithm ◽  
Structure Optimization ◽  
Probabilistic Neural Networks ◽  
Radial Basis ◽  
Full Structure
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