Deep Neural Network (DNN) stands for multilayered Neural Network (NN) that is capable of progressively learn the more abstract and composite representations of the raw features of the input data received, with no need for any feature engineering. They are advanced NNs having repetitious hidden layers between the initial input and the final layer. The working principle of such a standard deep classifier is based on a hierarchy formed by the composition of linear functions and a defined nonlinear Activation Function (AF). It remains uncertain (not clear) how the DNN classifier can function so well. But it is clear from many studies that within DNN, the AF choice has a notable impact on the kinetics of training and the success of tasks. In the past few years, different AFs have been formulated. The choice of AF is still an area of active study. Hence, in this study, a novel deep Feed forward NN model with four AFs has been proposed for breast cancer classification: hidden layer 1: Swish, hidden layer, 2:-LeakyReLU, hidden layer 3: ReLU, and final output layer: naturally Sigmoidal. The purpose of the study is twofold. Firstly, this study is a step toward a more profound understanding of DNN with layer-wise different AFs. Secondly, research is also aimed to explore better DNN-based systems to build predictive models for breast cancer data with improved accuracy. Therefore, the benchmark UCI dataset WDBC was used for the validation of the framework and evaluated using a ten-fold CV method and various performance indicators. Multiple simulations and outcomes of the experimentations have shown that the proposed solution performs in a better way than the Sigmoid, ReLU, and LeakyReLU and Swish activation DNN in terms of different parameters. This analysis contributes to producing an expert and precise clinical dataset classification method for breast cancer. Furthermore, the model also achieved improved performance compared to many established state-of-the-art algorithms/models.

深度神经网络 (DNN) 代表多层神经网络 (NN)，它能够逐步学习接收到的输入数据的原始特征的更抽象和复合的表示，而无需任何特征工程。它们是高级 NN，在初始输入和最终层之间具有重复的隐藏层。这种标准深度分类器的工作原理基于由线性函数和定义的非线性激活函数 (AF) 组成的层次结构。仍然不确定（不清楚）DNN 分类器如何能够如此出色地发挥作用。但从许多研究中可以清楚地看出，在 DNN 中，AF 的选择对训练的动力学和任务的成功有显着影响。在过去几年中，已经制定了不同的 AF。AF 的选择仍然是一个积极研究的领域。因此，在这项研究中，提出了一种具有四个 AF 的新型深度前馈神经网络模型用于乳腺癌分类：隐藏层 1：Swish、隐藏层、2：-LeakyReLU、隐藏层 3：ReLU，以及最终输出层：自然 Sigmoidal。研究的目的是双重的。首先，这项研究是朝着更深入理解具有分层不同 AF 的 DNN 迈出的一步。其次，研究还旨在探索更好的基于 DNN 的系统，以提高准确性为乳腺癌数据构建预测模型。因此，基准 UCI 数据集 WDBC 用于框架的验证，并使用十倍 CV 方法和各种性能指标进行评估。多次模拟和实验结果表明，所提出的解决方案比 Sigmoid、ReLU、以及 LeakyReLU 和 Swish 激活 DNN 的不同参数。该分析有助于为乳腺癌提供专业且精确的临床数据集分类方法。此外，与许多已建立的最先进算法/模型相比，该模型还实现了改进的性能。