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Hyperspectral images classification for white blood cells with attention-aided convolutional neural networks and fusion network.
- Source :
-
Journal of Modern Optics . Mar2023, Vol. 70 Issue 6, p364-376. 13p. - Publication Year :
- 2023
-
Abstract
- The classification of White blood cells (WBCs) plays an important role. However, the traditional method of blood smear analysis is laborious. This paper presented a classification method of WBCs based on hyperspectral images and Deep learning (DL). The U-net network was proposed to extract spectral features of WBCs region of interest (ROI) under the pseudo-color images with specific hyperspectral images (420.8, 557.2 and 667.4 nm). For spectral features and the pseudo-colour images of hyperspectral data, attention-aided one and two-dimensional convolutional neural networks were applied to establish WBCs classification models, respectively. The overall average accuracy can reach 94.20% and 92.60%, respectively. A fusion network was constructed to make full use of the spectral and image spatial features, and its classification accuracy reached 96.20%. In terms of overall average accuracy, the fusion network model was the optimal, but for individual types of WBCs, each network had its own unique advantages. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09500340
- Volume :
- 70
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- Journal of Modern Optics
- Publication Type :
- Academic Journal
- Accession number :
- 171926279
- Full Text :
- https://doi.org/10.1080/09500340.2023.2248634