| Project/Area Number |
15560349
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
System engineering
|
|---|
| Research Institution | The University of Tokushima |
|---|
Principal Investigator |
JUNJI Ueno The University of Tokushima, School of Health Sciences, Professor, 医学部, 教授 (60116788)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
KONDO Tadashi The University of Tokushima, School of Health Sciences, Professor, 医学部, 教授 (80205559)
KONDO Kazuya The University of Tokushima, Graduate School of Medicine, Lecturer, 大学院・ヘルスバイオサイエンス研究部, 講師 (10263815)
|
|---|
| Project Period (FY) |
2003 – 2004
|
| Project Status |
Completed (Fiscal Year 2004)
|
| Budget Amount *help |
¥1,800,000 (Direct Cost: ¥1,800,000)
Fiscal Year 2004: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 2003: ¥1,200,000 (Direct Cost: ¥1,200,000)
|
|---|
| Keywords | Medical image recognition / Neural networks / X-ray CT image / GMDH |
|---|
| Research Abstract |
In this study, we developed the revised GMDH (Group Method of Data Handling)-type neural network algorithms which can automatically organize the optimum neural network architectures fitting the characteristics of X-ray CT images of the lungs and applied this developed algorism to medical image recognition of the lungs. In these algorithms, the optimum neural network architecture is automatically selected from three types of neural network architectures such as sigmoid function type neural networks, RBF (Radial Basis Function) type neural networks and polynomial type neural networks. Furthermore, the structural parameters such as the number of layers, the number of neurons in the hidden layers and optimum neuron architectures are automatically selected so as to minimize the prediction error criterion defined as AIC ( Akaike's Information Criterion) or PSS (Prediction Sum of Squares). These algorithms have another ability of self-selecting the optimum input variables from many image characteristics so as to minimize the prediction error criterion AIC or PSS. Therefore, we can easily these revised GMDH-type neural network algorithms to the medical image recognition because the optimum neural network architecture fitting the medical image characteristics is automatically organized.
In this study, we are applying these revised GMDH-type neural network algorithms to the medical image recognition of the lungs and we organize the optimum neural network architectures fitting the image characteristics of the lungs. By using the organized neural networks in the computer, the regions of the lungs of the X-ray CT images are automatically extracted with the good accuracy.
|
