Pulmonary Nodule Detection and Classification Using All-Optical Deep Diffractive Neural Network

Autor: Junjie Shao, Lingxiao Zhou, Sze Yan Fion Yeung, Ting Lei, Wanlong Zhang, Xiaocong Yuan
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Life, Vol 13, Iss 5, p 1148 (2023)
Druh dokumentu: article
ISSN: 13051148
2075-1729
DOI: 10.3390/life13051148
Popis: 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.
Databáze: Directory of Open Access Journals
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