School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, China.
State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, China.
Comput Intell Neurosci. 2022 Sep 27;2022:1036913. doi: 10.1155/2022/1036913. eCollection 2022.
Deep neural network is a complex pattern recognition network system. It is widely favored by scholars for its strong nonlinear fitting ability. However, training deep neural network models on small datasets typically realizes worse performance than shallow neural network. In this study, a strategy to improve the sparrow search algorithm based on the iterative map, iterative perturbation, and Gaussian mutation is developed. This optimized strategy improved the sparrow search algorithm validated by fourteen benchmark functions, and the algorithm has the best search accuracy and the fastest convergence speed. An algorithm based on the iterative map, iterative perturbation, and Gaussian mutation improved sparrow search algorithm is designed to optimize deep neural networks. The modified sparrow algorithm is exploited to search for the optimal connection weights of deep neural network. This algorithm is implemented for the esophageal cancer dataset along with the other six algorithms. The proposed model is able to achieve 0.92 under all the eight scoring criteria, which is better than the performance of the other six algorithms. Therefore, an optimized deep neural network based on an improved sparrow search algorithm with iterative map, iterative perturbation, and Gaussian mutation is an effective approach to predict the survival rate of esophageal cancer.
深度神经网络是一种复杂的模式识别网络系统。由于其具有很强的非线性拟合能力，因此受到学者们的广泛青睐。然而，在小数据集上训练深度神经网络模型通常会导致性能比浅层神经网络差。在本研究中，开发了一种基于迭代映射、迭代扰动和高斯突变的改进麻雀搜索算法的策略。该优化策略通过十四种基准函数验证了麻雀搜索算法的改进，并具有最佳的搜索精度和最快的收敛速度。设计了一种基于迭代映射、迭代扰动和高斯突变的改进麻雀搜索算法来优化深度神经网络。修改后的麻雀算法用于搜索深度神经网络的最优连接权重。该算法在食管癌数据集上与其他六个算法一起实现。该模型在所有八个评分标准下的表现均达到 0.92，优于其他六个算法的性能。因此，基于改进的麻雀搜索算法，采用迭代映射、迭代扰动和高斯突变优化深度神经网络是预测食管癌生存率的有效方法。
