Application of convolutional neural network in signal classification for in vivo photoacoustic flow cytometry

Ziran Song; Yuting Fu; Jiaqi Qu; Qi Liu; Xunbin Wei

doi:10.1117/12.2576784

10 October 2020 Application of convolutional neural network in signal classification for in vivo photoacoustic flow cytometry

Ziran Song, Yuting Fu, Jiaqi Qu, Qi Liu, Xunbin Wei

Author Affiliations +

Proceedings Volume 11553, Optics in Health Care and Biomedical Optics X; 115532W (2020) https://doi.org/10.1117/12.2576784
Event: SPIE/COS Photonics Asia, 2020, Online Only

Conference Poster

Abstract

Melanoma is one of the most serious skin cancers in the world. As circulating tumor cells have been proved to be an important marker of early metastasis of cancer, the detection of circulating tumor cells of melanoma is of great significance for early diagnosis and the monitoring of tumor progression. In vivo photoacoustic flow cytometry (PAFC) is constructed to achieve real-time and non-invasive detection of circulating melanoma cells in vivo. However, as the photoacoustic signals acquired in the detection process are disturbed by various kinds of noise, it is difficult to accurately distinguish the photoacoustic signals of background and circulating tumor cells by the traditional triple mean square deviation method. Therefore, a photoacoustic signal classification method is proposed based on convolutional neural network, which can greatly improve the accuracy of detection. Features of signals are extracted by the convolutional neural network to distinguish photoacoustic signals of melanoma cells and background. We construct a convolutional neural network based on one-dimensional input signals. For training the classifier, a large number of samples are selected. The accuracy rate in the test set can reach 95%. Besides, a neural network is built based on VGG16 model and transfer learning, and the trained classifier can realize the accuracy of 98% in the test set. Experiments show that the method of photoacoustic signal classification based on convolutional neural network greatly improves the accuracy of signal classification, and realizes the rapid and accurate analysis of a large number of data.

Conference Presentation

Citation Download Citation

Ziran Song, Yuting Fu, Jiaqi Qu, Qi Liu, and Xunbin Wei "Application of convolutional neural network in signal classification for in vivo photoacoustic flow cytometry", Proc. SPIE 11553, Optics in Health Care and Biomedical Optics X, 115532W (10 October 2020); https://doi.org/10.1117/12.2576784

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