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The emerging role of deep learning in cytology
Author(s) -
Dey Pranab
Publication year - 2021
Publication title -
cytopathology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.512
H-Index - 48
eISSN - 1365-2303
pISSN - 0956-5507
DOI - 10.1111/cyt.12942
Subject(s) - convolutional neural network , artificial intelligence , deep learning , grading (engineering) , computer science , boltzmann machine , cytology , artificial neural network , deep belief network , pattern recognition (psychology) , machine learning , feature extraction , medicine , pathology , civil engineering , engineering
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.