Artificial convolution neural network with wavelet kernels for disease pattern recognition

Shih-Chung Benedict Lo; Huai Li; Jyh-Shyan Lin; Akira Hasegawa; Chris Yuzheng Wu; Matthew T. Freedman M.D.; Seong Ki Mun

doi:10.1117/12.208730

12 May 1995 Artificial convolution neural network with wavelet kernels for disease pattern recognition

Shih-Chung Benedict Lo, Huai Li, Jyh-Shyan Lin, Akira Hasegawa, Chris Yuzheng Wu, Matthew T. Freedman M.D., Seong Ki Mun

Author Affiliations +

Proceedings Volume 2434, Medical Imaging 1995: Image Processing; (1995) https://doi.org/10.1117/12.208730
Event: Medical Imaging 1995, 1995, San Diego, CA, United States

Abstract

A two-dimensional convolution neural network (CNN) with wavelet kernels (WK) has been developed for image pattern recognition. The structure of the CNN is a simplified version of the neocognitron. We used only a two-level structure and eliminated all complex-cell layers. Nets between two adjacent layers in the feature selection level of the CNN are selectively interconnected across groups. In this part of the CNN signals processing, each group in the receiving layer receives signals from a group of weights (i.e., kernels). For the forward signal propagation, the product obtained from the kernel convoluting the front layer is collected onto the corresponding matrix element of the receiving layer. In this paper, the convolution kernels of the CNN (CNN/WK) are wavelet based and are trained by a supervised manner. In the development of the CNN/WK, we forced each updated convolution kernel to be orthonormal. Therefore, features (transformed coefficients) selected on the transform domain are linearly independent. Hence, the fully connected layers in the classification level of the CNN can perform more effectively. The applications of the CNN for disease pattern recognition have been very successful. When isolated patterns were further processed by internal filtering and classification layers were built into the neural network structure, the disease patterns were more easily recognized. Although, we did not receive substantial improvement of the ROC performance using the CNN/WK, this method may assist us in the analysis of the trained kernels and eventually lead to the optimization of feature extraction in a course of disease pattern recognition.

Citation Download Citation

Shih-Chung Benedict Lo, Huai Li, Jyh-Shyan Lin, Akira Hasegawa, Chris Yuzheng Wu, Matthew T. Freedman M.D., and Seong Ki Mun "Artificial convolution neural network with wavelet kernels for disease pattern recognition", Proc. SPIE 2434, Medical Imaging 1995: Image Processing, (12 May 1995); https://doi.org/10.1117/12.208730

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