As a result, the number of total parameters was reduced by almost 100%35 and classification accuracy was improved. The total number of parameters used was 39,553. In this architecture, we took image information more directly after applying max-pooling and merged it with information generated after convolutions. Features in the convolution layer are more generic (e.g., blobs, textures, edges, etc.). So, adding image information directly will create more specific information for each case. After merging, another convolution and max pooling layer before the final classification layer maintains the generic information about the image and can provide more features of about the image for getting a better classification result. Figure 3 shows a flowchart of CNN Architecture 3. 解释

这段文本介绍了一个卷积神经网络（CNN）的第三种架构。在这个架构中，研究人员通过应用最大池化操作直接获取图像信息，并将其与卷积生成的信息合并在一起。卷积层中的特征更加通用（例如，斑点、纹理、边缘等），因此直接添加图像信息将为每种情况创建更具体的信息。在合并后，另一个卷积和最大池化层在最终分类层之前维护图像的通用信息，并能够提供更多关于图像的特征以获得更好的分类结果。文中的图3展示了CNN架构3的流程图。