Screening COVID-19 from chest X-ray images by an optical diffractive neural network with the optimized F number  

作　　者：JIALONG WANG SHOUYU CHAI WENTING GU BOYI LI XUE JIANG YUNXIANG ZHANG HONGEN LIAO XIN LIU DEAN TA 

机构地区：[1]Academy for Engineering and Technology,Fudan University,Shanghai 200433,China [2]Center for Biomedical Engineering,Fudan University,Shanghai 200433,China [3]Department of Chemistry,Fudan University,Shanghai 200433,China [4]Department of Biomedical Engineering,School of Medicine,Tsinghua University,Beijing 100084,China

出　　处：《Photonics Research》2024年第7期1410-1426,共17页光子学研究（英文版）

基　　金：National Natural Science Foundation of China(12274092);Natural Science Foundation of Shanghai Municipality (21ZR1405200)。

摘　　要：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 threeclassification 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.

关 键 词：NEURAL network offering 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术] R816.4[医药卫生—放射医学]