Medical image diagnosis of lung cancer by multi-layered GMDH-type neural network self-selecting functions

被引：6
作者：
Kondo T. [1 ]
Ueno J. [1 ]
Takao S. [1 ]
机构：
[1] Graduate School of Health Sciences, The University of Tokushima, Tokushima, 770-8509
来源：
Kondo, T. (kondo@medsci.tokushima-u.ac.jp) | 1600年 / Springer Japan卷 / 18期
基金：
日本学术振兴会;
关键词：
GMDH; Medical image diagnosis; Neural networks;
D O I：
10.1007/s10015-013-0094-0
中图分类号：
学科分类号：
摘要：
In this study, a revised Group Method of Data Handling (GMDH)-type neural network self-selecting functions is applied to the computer aided image diagnosis (CAD) of lung cancer. The GMDH-type neural network algorithm has an ability of self-selecting optimum neural network architecture from three neural network architectures, such as sigmoid function neural network, radial basis function neural network and polynomial neural network. The GMDH-type neural network also has abilities of self-selecting the number of layers, the number of neurons in hidden layers and useful input variables. This algorithm is applied to CAD of lung cancers, and it is shown that this algorithm is useful for the CAD, and is very easy to apply to practical complex problems because optimum neural network architecture is automatically organized. © 2013 ISAROB.
引用
收藏
页码：20 / 26
页数：6
相关论文
共 11 条
  • [1] Stoitsis J., Valavanis J., Et al., Computer aided diagnosis based on medical image processing and artificial intelligence methods, Nuclear Instrum Methods Phys Research A, 569, pp. 591-595, (2006)
  • [2] Doi K., Current status and future potential of computer-aided diagnosis in medical imaging, Br J Radiol, 78, (2005)
  • [3] Mougiakakou S.G., Valavanis L.K., Et al., Differential diagnosis of CT focal liver lesions using textures, feature selection and ensemble driven classifiers, Artif Intell Med, 41, pp. 25-37, (2007)
  • [4] Hung Y.L., Chen J.H., Shen W.C., Diagnosis of hepatic tumors with texture analysis in nonenhanced computed tomography images, Acad Radiol, 13, 6, pp. 713-720, (2006)
  • [5] Kondo T., Ueno J., Multi-layered GMDH-type neural network self-selecting optimum neural network architecture and its application to 3-dimensional medical image recognition of blood vessels, Int J Innov Comput Inf Control, 4, 1, pp. 175-187, (2008)
  • [6] Kondo T., GMDH neural network algorithm using the heuristic self-organization method and its application to the pattern identification problem, Proceedings of the 37th SICE Annual Conference, pp. 1143-1148, (1998)
  • [7] Farlow S.J., Self-Organizing Methods in Modeling, GMDH-Type Algorithm, (1984)
  • [8] Ivakhnenko A.G., Heuristic self-organization in problems of engineering cybernetics, Automatica, 6, 2, pp. 207-219, (1970)
  • [9] Akaike H., A new look at the statistical model identification, IEEE Trans Autom Control, 19, 6, pp. 716-723, (1974)
  • [10] Tamura H., Kondo T., Heuristics free group method of data handling algorithm of generating optimum partial polynomials with application to air pollution prediction, Int J Syst Sci, 11, 9, pp. 1095-1111, (1980)
12