基于机器学习和深度学习的癌症诊断和分类模型综述

Q3 Chemistry
C. Suguna, S. Balamurugan
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引用次数: 3

摘要

子宫颈癌是妇女常见的致命疾病,需要及早诊断以降低发病率。巴氏涂片被认为是一种广泛使用的筛查和诊断宫颈癌的技术。由于传统的人工筛查技术在宫颈癌诊断方面效率低下,一些研究工作已经开始开发用于宫颈癌诊断的自动机器学习(ML)和深度学习(DL)工具。本文综述了近年来宫颈癌的诊断和分类工作。本文对近年来宫颈癌诊断和分类的ML和DL模型进行了详细的综述。此外,本文还对宫颈癌诊断的分割技术进行了综述。在调查的最后,进行了一个简短的比较研究,以确定所审查的方法的意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Extensive Review on Machine Learning and Deep Learning Based Cervical Cancer Diagnosis and Classification Models
Cervical cancer is a commonly occurring deadliest disease among women, which needs earlier diagnosis to reduce the prevalence. Pap-smear is considered as a widely employed technique to screen and diagnose cervical cancer. Since classical manual screening techniques are inefficient in the identification of cervical cancer, several research works have been started to develop automated machine learning (ML) and deep learning (DL) tools for cervical cancer diagnosis. This paper surveys the recent works made on cervical cancer diagnosis and classification. The recently presently ML and DL models for cervical cancer diagnosis and classification has been reviewed in detail. Besides, segmentation techniques developed for cervical cancer diagnosis also surveyed. At the end of the survey, a brief comparative study has been carried out to identify the significance of the reviewed methods.
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来源期刊
Journal of Computational and Theoretical Nanoscience
Journal of Computational and Theoretical Nanoscience 工程技术-材料科学:综合
自引率
0.00%
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0
审稿时长
3.9 months
期刊介绍: Information not localized
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