Pulmonary Nodule Detection and Classification Using All-Optical Deep Diffractive Neural Network
Author:
Shao Junjie1, Zhou Lingxiao1ORCID, Yeung Sze Yan Fion2, Lei Ting1, Zhang Wanlong1ORCID, Yuan Xiaocong13
Affiliation:
1. Nanophotonics Research Center, Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen 518060, China 2. State Key Laboratory on Advanced Displays and Optoelectronics Technologies, Department of Electronic & Computer Engineering, Hong Kong University of Science and Technology, Hong Kong SAR, China 3. Research Center for Humanoid Sensing, Research Institute of Intelligent Sensing, Zhejiang Lab, Hangzhou 311100, China
Abstract
A deep diffractive neural network (D2NN) is a fast optical computing structure that has been widely used in image classification, logical operations, and other fields. Computed tomography (CT) imaging is a reliable method for detecting and analyzing pulmonary nodules. In this paper, we propose using an all-optical D2NN for pulmonary nodule detection and classification based on CT imaging for lung cancer. The network was trained based on the LIDC-IDRI dataset, and the performance was evaluated on a test set. For pulmonary nodule detection, the existence of nodules scanned from CT images were estimated with two-class classification based on the network, achieving a recall rate of 91.08% from the test set. For pulmonary nodule classification, benign and malignant nodules were also classified with two-class classification with an accuracy of 76.77% and an area under the curve (AUC) value of 0.8292. Our numerical simulations show the possibility of using optical neural networks for fast medical image processing and aided diagnosis.
Funder
Guangdong Major Project of Basic and Applied Basic Research National Natural Science Foundation of China Key Research Project of Zhejiang Lab Zhejiang Lab Open Research Project State Key Laboratory of Advanced Displays and Optoelectronics Technologies Shenzhen Science and Technology Innovation Commission Shenzhen Newly Introduced High-End Talents Research Startup Project
Subject
Paleontology,Space and Planetary Science,General Biochemistry, Genetics and Molecular Biology,Ecology, Evolution, Behavior and Systematics
Reference53 articles.
1. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process Syst. 2. Sutskever, I., Vinyals, O., and Le, Q.V. (2017). Sequence to sequence learning with neural networks. Adv. Neural Inf. Process Syst. 3. Zhang, T., Ye, W., Yang, B., Zhang, L., Ren, X., Liu, D., Sun, J., Zhang, S., Zhang, H., and Zhao, W. (2022, January 22). Frequency-Aware Contrastive Learning for Neural Machine Translation. Proceedings of the AAAI Conference on Artificial Intelligence, Online. 4. ImageNet classification with deep convolutional neural networks;Krizhevsky;Commun. ACM,2017 5. Ioffe, S., and Szegedy, C. (2015, January 6). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on Machine Learning, Lille, France.
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