Background and Objective: One of the significant retinal diseases that affected older people is called Age-related Macular Degeneration (AMD). The first stage creates a blur effect on vision and later leads to central vision loss. Most people overlooked the primary stage blurring and converted it into an advanced stage. There is no proper treatment to cure the disease. So the early detection of AMD is essential to prevent its extension into the advanced stage. This paper proposes a novel deep Convolutional Neural Network (CNN) architecture to automate AMD diagnosis early from Optical Coherence Tomographic (OCT) images.

Methods: The proposed architecture is a multiscale and multipath CNN with six convolutional layers. The multiscale convolution layer permits the network to produce many local structures with various filter dimensions. The multipath feature extraction permits CNN to merge more features regarding the sparse local and fine global structures. The performance of the proposed architecture is evaluated through ten-fold cross-validation methods using different classifiers like support vector machine, multi-layer perceptron, and random forest.

Results: The proposed CNN with the random forest classifier gives the best classification accuracy results. The proposed method is tested on data set 1, data set 2, data set 3, data set 4, and achieved an accuracy of 0.9666, 0.9897, 0.9974, and 0.9978 respectively, with random forest classifier. Also, we tested the combination of first three data sets and achieved an accuracy of 0.9902.

Conclusions: An efficient algorithm for detecting AMD from OCT images is proposed based on a multiscale and multipath CNN architecture. Comparison with other approaches produced results that exhibit the efficiency of the proposed algorithm in the detection of AMD. The proposed architecture can be applied in rapid screening of the eye for the early detection of AMD. Due to less complexity and fewer learnable parameters.

背景与目的：影响老年人的重要视网膜疾病之一被称为年龄相关性黄斑变性（AMD）。第一阶段对视力产生模糊效果，随后导致中央视力丧失。大多数人忽略了初级阶段的模糊，并将其转换为高级阶段。没有适当的治疗方法可以治愈这种疾病。所以早期发现AMD对于防止其扩展到晚期至关重要。本文提出了一种新颖的深度卷积神经网络 (CNN) 架构，可从光学相干断层扫描 (OCT) 图像中自动进行早期 AMD 诊断。

方法：所提出的架构是一个具有六个卷积层的多尺度和多路径 CNN。多尺度卷积层允许网络产生许多具有不同滤波器维度的局部结构。多路径特征提取允许 CNN 合并更多关于稀疏局部和精细全局结构的特征。使用支持向量机、多层感知器和随机森林等不同分类器，通过十倍交叉验证方法评估所提出架构的性能。

结果：提出的带有随机森林分类器的 CNN 给出了最好的分类精度结果。所提出的方法在数据集 1、数据集 2、数据集 3、数据集 4 上进行了测试，使用随机森林分类器分别达到了 0.9666、0.9897、0.9974 和 0.9978 的准确率。此外，我们测试了前三个数据集的组合，并达到了 0.9902 的准确度。

结论：基于多尺度和多路径 CNN 架构，提出了一种从 OCT 图像中检测 AMD 的有效算法。与其他方法的比较产生的结果显示了所提出的算法在检测 AMD 方面的效率。所提出的架构可应用于眼睛的快速筛查以早期检测 AMD。由于较低的复杂性和较少的可学习参数。