Differentiation of two subtypes of adult hydrocephalus by mixture of experts.
This paper illustrates the use of mixture of experts (ME) network structure to guide model selection for diagnosis of two subtypes of adult hydrocephalus (normal-pressure hydrocephalus-NPH and aqueductal stenosis-AS). The ME is a modular neural network architecture for supervised learning. Expectation-Maximization (EM) algorithm was used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. To improve classification accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. The classifiers were trained on the defining features of NPH and AS (velocity and flux). Three types of records (normal, NPH and AS) were classified with the accuracy of 95.83% by the ME network structure. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network models.
Ubeyli
Elif Derya
ED
Department of Electrical and Electronics Engineering, Faculty of Engineering, TOBB Ekonomi ve Teknoloji Universitesi, Ankara, Turkey. edubeyli@etu.edu.tr
Ilbay
Konuralp
K
Ilbay
Gul
G
Sahin
Deniz
D
Akansel
Gur
G
D000293:Adolescent; D000328:Adult; D000368:Aged; D005103:Expert Systems; D005260:Female; D006801:Humans; D006849:Hydrocephalus / Q000145:classification* / Q000175:diagnosis; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D012372:ROC Curve; D012189:Retrospective Studies; D055815:Young Adult