Deep learning (DL) is a component or subset of artificial intelligence. DL has contributed significant change in feature extraction and image classification. Various algorithmic models are used in DL such as a convolutional neural network (CNN), recurrent neural network, restricted Boltzmann machine, deep belief network and autoencoders. Of these, CNN is the most commonly used algorithm in the field of pathology for feature extraction and building neural network models. DL may be useful for tumour diagnosis, classification of the tumour and grading of the tumour in cytology. In this brief review, the basic concept of the DL and CNN are described. The application, prospects and challenges of the DL in the cytology are also discussed.

中文翻译：

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深度学习在细胞学中的新兴作用

深度学习 (DL) 是人工智能的一个组成部分或子集。DL 在特征提取和图像分类方面做出了重大改变。深度学习中使用了各种算法模型，例如卷积神经网络 (CNN)、循环神经网络、受限玻尔兹曼机、深度置信网络和自动编码器。其中，CNN 是病理学领域最常用的算法，用于特征提取和构建神经网络模型。DL 可用于肿瘤诊断、肿瘤分类和细胞学中的肿瘤分级。在这篇简短的评论中，描述了 DL 和 CNN 的基本概念。还讨论了DL在细胞学中的应用、前景和挑战。