A novel voting convergent difference neural network for diagnosing breast cancer

摘要

Breast cancer is one of the most frequently occurred cancers for females, and thus diagnosing breast cancer is very important. Neural dynamic algorithm (NDA) has been successfully applied in many fields with the characteristics of parallel computing and exponential convergence. However, there is no research on applying NDA-based neural network for pattern classification. In this paper, a novel voting convergent difference neural network (V-CDNN) is proposed. To do so, samples are firstly handled by feature selection, feature weighting and sample normalization. Secondly, the preprocessed samples are used to simultaneously and independently train several convergent difference neural networks in different types of mapping functions. Thirdly, in the testing process, voting strategy for these networks is applied to make diagnosis results more accurate and convincing. Being different from most existing neural networks, the proposed V-CDNN adopts neural dynamic learning algorithm, which greatly improves computation efficiency and increases accuracy rate of diagnosis. Experimental results verify that the proposed V-CDNN can achieve 100% average diagnosis accuracy, which is the highest among existing state-of-the-art methods on the open data set.Breast cancer is one of the most frequently occurred cancers for females, and thus diagnosing breast cancer is very important. Neural dynamic algorithm (NDA) has been successfully applied in many fields with the characteristics of parallel computing and exponential convergence. However, there is no research on applying NDA-based neural network for pattern classification. In this paper, a novel voting convergent difference neural network (V-CDNN) is proposed. To do so, samples are firstly handled by feature selection, feature weighting and sample normalization. Secondly, the preprocessed samples are used to simultaneously and independently train several convergent difference neural networks in different types of mapping functions. Thirdly, in the testing process, voting strategy for these networks is applied to make diagnosis results more accurate and convincing. Being different from most existing neural networks, the proposed V-CDNN adopts neural dynamic learning algorithm, which greatly improves computation efficiency and increases accuracy rate of diagnosis. Experimental results verify that the proposed V-CDNN can achieve 100% average diagnosis accuracy, which is the highest among existing state-of-the-art methods on the open data set. ? 2021 Elsevier B.V. All rights reserved.