College of Computer Information and Engineering, Nanchang Institute of Technology, Nanchang 330044, China.
Comput Intell Neurosci. 2022 Aug 31;2022:4600840. doi: 10.1155/2022/4600840. eCollection 2022.
The detection and classification of histopathological cell images is a hot topic in current research. Medical images are an important research direction and are widely used in computer-aided diagnosis, biological research, and other fields. A neural network model based on deep learning is also common in medical image analysis and automatic detection and classification of tissue and cell images. Current medical cell detection methods generally do not consider that the yield is affected by other factors in the topological region, which leads to inevitable errors in the accuracy and generalization of the algorithm; at the same time, the current medical cell imaging methods are too simple to predict the classification markers, which affect the accuracy of cell image classification. This study introduces the concepts of two kinds of neural networks and then constructs a cell recognition model based on the convolution neural network principle and staining principle. In the experimental part, we developed three groups of experiments using the same equation as the experiment and tested the best cell recognition model proposed in this study.
组织细胞图像的检测和分类是当前研究的热点。医学图像是一个重要的研究方向,广泛应用于计算机辅助诊断、生物研究等领域。基于深度学习的神经网络模型在医学图像分析和组织及细胞图像的自动检测和分类中也很常见。目前的医学细胞检测方法通常没有考虑到在拓扑区域中其他因素对产量的影响,这导致算法的准确性和泛化性不可避免地出现误差;同时,目前的医学细胞成像方法过于简单,无法预测分类标记物,影响了细胞图像分类的准确性。本研究介绍了两种神经网络的概念,然后基于卷积神经网络原理和染色原理构建了细胞识别模型。在实验部分,我们使用相同的方程进行了三组实验,测试了本研究提出的最佳细胞识别模型。