Hyperspectral images classification for white blood cells with attention-aided convolutional neural networks and fusion network

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.