With the advancement in technology and computation capabilities, identifying retinal damage through state-of-the-art CNNs architectures has led to the speedy and precise diagnosis, thus inhibiting further disease development. In this study, we focus on the classification of retinal damage caused by detecting choroidal neovascularization (CNV), diabetic macular edema (DME), DRUSEN, and NORMAL in optical coherence tomography (OCT) images. The emphasis of our experiment is to investigate the component of depth in the neural network architecture. We introduce a shallow convolution neural network - LightOCT, outperforming the other deep model configurations, with the lowest value of LVCEL and highest accuracy (+98\% in each class). Next, we experimented to find the best fit optimizer for LightOCT. The results proved that the combination of LightOCT and Adam gave the most optimal results. Finally, we compare our approach with transfer learning models, and LightOCT outperforms the state-of-the-art models in terms of computational cost, least training time and gives comparable results in the criteria of accuracy. We would direct our future work to improve the accuracy metrics with shallow models such that the trade-off between training time and accuracy is reduced.

中文翻译：

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LightOCT：探索视网膜疾病检测的深度

随着技术和计算能力的进步，通过最先进的 CNN 架构识别视网膜损伤已经导致快速和精确的诊断，从而抑制疾病的进一步发展。在这项研究中，我们专注于在光学相干断层扫描 (OCT) 图像中检测脉络膜新生血管 (CNV)、糖尿病性黄斑水肿 (DME)、玻璃膜疣和 NORMAL 引起的视网膜损伤的分类。我们实验的重点是研究神经网络架构中深度的组成部分。我们引入了一个浅层卷积神经网络 - LightOCT，其性能优于其他深度模型配置，具有最低的 LVCEL 值和最高的准确度（每个类别中 +98%）。接下来，我们尝试找到最适合 LightOCT 的优化器。结果证明 LightOCT 和 Adam 的组合给出了最优化的结果。最后，我们将我们的方法与迁移学习模型进行比较，LightOCT 在计算成本、最少的训练时间和准确度标准方面都优于最先进的模型。我们将指导我们未来的工作以提高浅层模型的准确性指标，从而减少训练时间和准确性之间的权衡。