ArticleLife (Basel, Switzerland)2023
Pulmonary Nodule Detection and Classification Using All-Optical Deep Diffractive Neural Network.
Article in Life (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Diffractive Optical Neural Networks: Pathways Toward Intelligent and Scalable Photonic Computing.Nanophotonics (Berlin, Germany) · 2026Review
- Aided diagnosis of thyroid nodules based on an all-optical diffraction neural network.Quantitative imaging in medicine and surgery · 2023Article
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Authors and funding
6 authors.
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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.
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