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An New Automatic Nucleated Cell Counting Method With Improved Cellular Neural Networks (ICNN)

机译:具有改进的蜂窝神经网络(ICNN)的一种新的自动核细胞计数方法

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The number of nucleated cells in a bone marrow slice image is an important sign of the degree of hyperplasia. But because of the complex components in the bone marrow slice image, there are only a few methods available for cell detection or segmentation with poor effect. This paper focuses on this issue. A new automatic nucleated cell counting method based on improve cellular neural networks (ICNN) is proposed. We improve the CNN output function and practically design all the templates, then prove the stability. The process details are also presented. Experimental results show good performance. ICNN can detect almost all nucleated cells. Its running speed is expected to be comparatively high due to the easy hardware implementation and high speed of CNN
机译:骨髓切片图像中有核细胞的数量是增生程度的重要标志。 但由于骨髓切片图像中的复杂组件,只有几种可用于细胞检测或分割的方法,效果不佳。 本文重点关注此问题。 提出了一种基于改进蜂窝神经网络(ICNN)的新的自动核细胞计数方法。 我们改进了CNN输出功能,实际上设计了所有模板,然后证明了稳定性。 还提出了过程细节。 实验结果表现出良好的性能。 ICNN可以检测几乎所有有核细胞。 由于易于硬件实现和高速CNN的硬件实现,它的运行速度预计将相对较高

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