Potential usefulness of an artificial neural network for assessing ventricular size.
An artificial neural network approach was applied to assess ventricular size from computed tomograms. Three layer, feed-forward neural networks with a back propagation algorithm were designed to distinguish between three degrees of enlargement of the ventricles on the basis of patient's age and six items of computed tomographic information. Data for training and testing the neural network were created with computed tomograms of the brains selected at random from daily examinations. Four radiologists decided by mutual consent subjectively based on their experience whether the ventricles were within normal limits, slightly enlarged, or enlarged for the patient's age. The data for training was obtained from 38 patients. The data for testing was obtained from 47 other patients. The performance of the neural network trained using the data for training was evaluated by the rate of correct answers to the data for testing. The valid solution ratio to response of the test data obtained from the trained neural networks was more than 90% for all conditions in this study. The solutions were completely valid in the neural networks with two or three units at the hidden layer with 2,200 learning iterations, and with two units at the hidden layer with 11,000 learning iterations. The squared error decreased remarkably in the range from 0 to 500 learning iterations, and was close to constant over two thousand learning iterations. The neural network with a hidden layer having two or three units showed high decision performance. The preliminary results strongly suggest that the neural network approach has potential utility in computer-aided estimation of enlargement of the ventricles.
Fukuda
H
H
Department of Radiology, Kobe City General Hospital, Japan.
Inoue
Y
Y
Nakajima
H
H
Usuki
N
N
Saiwai
S
S
Miyamoto
T
T
Onoyama
Y
Y
D002552:Cerebral Ventricles / Q000033:anatomy & histology*; D014695:Cerebral Ventriculography; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016571:Neural Networks, Computer; D014057:Tomography, X-Ray Computed