期刊
PATTERN RECOGNITION
卷 115, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2021.107885
关键词
Environmental miroorganisms; Image segmentation; Deep convolutional neural networks; Low-cost
资金
- National Natural Science Foundation of China [61806047]
In this paper, a novel Low-cost U-Net (LCU-Net) is proposed for environmental microorganism image segmentation task. The LCU-Net addresses the memory cost issue of traditional U-Net and demonstrates effectiveness and potential in practical application.
In this paper, we propose a novel Low-cost U-Net (LCU-Net) for the Environmental Microorganism (EM) image segmentation task to assist microbiologists in detecting and identifying EMs more effectively. The LCU-Net is an improved Convolutional Neural Network (CNN) based on U-Net, Inception, and concatenate operations. It addresses the limitation of single receptive field setting and the relatively high memory cost of U-Net. Experimental results show the effectiveness and potential of the proposed LCU-Net in the practical EM image segmentation field. (c) 2021 Elsevier Ltd. All rights reserved.
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