Abstract
Recent developments in convolutional neural network (CNN) led to an interest in the classification of blood cells hyperspectral images (HSI). However, traditional CNN algorithms cannot explore the intrinsic geometric structures of blood cells HSI, which may cause a limit in classification accuracy. To address this issue, this paper proposed a three-dimensional (3-D) convolutional neural network driven by dimensionality reduction termed 3DDRNet. 3DDRNet first designs a optimization criteria to compact intraclass neighbors and separate interclass samples in low-dimensional embedding space. Then, a 3-D convolutional neural network is used to extract spatial-spectral features for classification. Experimental results on the Bloodcell1-3 and Bloodcell2-2 datasets demonstrate that the proposed 3DDRNet can achieve better classification results than many state-of-the-art methods.
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Acknowledgments
The authors would like to thank Professors Li Wei of Beijing Institute of Technology for providing us with the blood cells hyperspectral image. Thanks to the anonymous reviewers and the associate editor for their insightful comments and suggestions. This work was supported in part by the National Science Foundation of China under Grant 42071302, the Innovation Program for Chongqing Overseas Returnees under Grant cx2019144, and the Higher Education and Research Grants of NVIDIA.
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Yuan Li: Methodology, Validation, Data curation, Writing-review, Writing-original draft. Hong Huang: Supervision, Investigation, Methodology, Validation, Formal analysis, Writing-review & editing. Jian Wu: Methodology, Formal analysis, Validation, Writing-review. Yiming Tang: Formal analysis, Validation, Writing-review
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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Li, Y., Huang, H., Wu, J., Tang, Y. (2021). 3-D Convolutional Neural Network Driven by Dimensionality Reduction for Hyperspectral Blood Cells Classification. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12889. Springer, Cham. https://doi.org/10.1007/978-3-030-87358-5_59
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