Résumé

This paper introduces an optimized RBF-Neural Network for breast cancer classification. The study is based on the optimization of the network through three learning phases. In the first phase, K-means clustering method is used to define RBFs centers. In the second phase, Particle Swarm Optimization is used to optimize RBFs widths. In this phase, a pseudo inverse solution is used to calculate the output weights. Finally, in the third phase, the back-propagation algorithm is used for fine-tuning the obtained parameters, namely centers, widths and output weights. The back-propagation is then initialized with the obtained parameters instead of a random initialization. To evaluate the performance of the proposed method, tests were performed using the Wisconsin Diagnostic Breast Cancer database. The proposed system was compared with a network trained only with BP and a network trained with K-means + PSO. The results obtained are promising compared to other advanced methods, and the proposed learning method gives better results by combining these three methods.

Information

Item Type:	Journal
Divisions:	» Faculté des Sciences et de la Technologie
ePrint ID:	4422
Date Deposited:	2023-09-15
Further Information:	Google Scholar
URI:	https://www.univ-soukahras.dz/fr/publication/article/4422

BibTex