The COVID-19 pandemic continues to significantly impact people’s lives worldwide, emphasizing the critical need for effective detection methods. Many existing deep learning-based approaches for COVID-19 detection offer high accuracy but demand substantial computing resources, time, and energy. In this study, we introduce an optical diffractive neural network (ODNN-COVID), which is characterized by low power consumption, efficient parallelization, and fast computing speed for COVID-19 detection. In addition, we explore how the physical parameters of ODNN-COVID affect its diagnostic performance. We identify the F number as a key parameter for evaluating the overall detection capabilities. Through an assessment of the connectivity of the diffractive network, we established an optimized range of F number, offering guidance for constructing optical diffractive neural networks. In the numerical simulations, a three-layer system achieves an impressive overall accuracy of 92.64% and 88.89% in binary- and three-classification diagnostic tasks. For a single-layer system, the simulation accuracy of 84.17% and the experimental accuracy of 80.83% can be obtained with the same configuration for the binary-classification task, and the simulation accuracy is 80.19% and the experimental accuracy is 74.44% for the three-classification task. Both simulations and experiments validate that the proposed optical diffractive neural network serves as a passive optical processor for effective COVID-19 diagnosis, featuring low power consumption, high parallelization, and fast computing capabilities. Furthermore, ODNN-COVID exhibits versatility, making it adaptable to various image analysis and object classification tasks related to medical fields owing to its general architecture.

中文翻译：

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通过具有优化 F 值的光学衍射神经网络从胸部 X 射线图像中筛查 COVID-19


COVID-19 大流行继续对全世界人们的生活产生重大影响，凸显了对有效检测方法的迫切需求。许多现有的基于深度学习的 COVID-19 检测方法可提供高精度，但需要大量的计算资源、时间和精力。在本研究中，我们介绍了一种光学衍射神经网络（ODNN-COVID），其具有低功耗、高效并行化和计算速度快的特点，可用于COVID-19检测。此外，我们还探讨了 ODNN-COVID 的物理参数如何影响其诊断性能。我们将F数确定为评估整体检测能力的关键参数。通过对衍射网络连通性的评估，我们建立了F数的优化范围，为构建光学衍射神经网络提供了指导。在数值模拟中，三层系统在二分类和三分类诊断任务中取得了令人印象深刻的总体准确率，分别为 92.64% 和 88.89%。对于单层系统，在相同的配置下，二分类任务的模拟精度为84.17%，实验精度为80.83%，而二分类任务的仿真精度为80.19%，实验精度为74.44%。三分类任务。仿真和实验均验证了所提出的光学衍射神经网络可作为有效的 COVID-19 诊断的无源光学处理器，具有低功耗、高并行化和快速计算能力。此外，ODNN-COVID 表现出多功能性，由于其通用架构，使其能够适应与医学领域相关的各种图像分析和对象分类任务。