基于图像的深度学习宫颈癌检测与诊断研究综述

Oluwatomisin E. Aina, Steve A. Adeshina, A. Aibinu
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引用次数: 9

摘要

宫颈癌是女性中第四大最常见的癌症类型,2018年全球有57万例发病率和31.1万例死亡。它是由一种叫做人类乳头瘤病毒(HPV)的病毒引起的。如果及早进行筛查,可以降低这种发病率。然而,由于某些因素的影响,人工筛查方法在检测子宫颈癌方面并不有效。然而,这导致误诊和过度治疗。因此,研究人员提出了结合传统和深度学习技术进行宫颈自动筛查。本文旨在回顾过去在深度学习领域所做的工作,并讨论宫颈癌自动检测的未来方向。据信,这将确保正确的诊断,并可能潜在地减少宫颈癌的患病率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning for Image-based Cervical Cancer Detection and Diagnosis — A Survey
Cervical cancer is the fourth most common type of cancer found in females with a record of 570,000 incidences and 311,000 deaths in the year 2018 worldwide. It is caused by a virus known as Human Papilloma Virus (HPV). Screening if done early can reduce this prevalence. However, manual screening methods are not efficient in the detection of cervical cancer as a result of some factors. This, however, results in misdiagnosis and over-treatment. Therefore, researchers proposed screening cervical automatically by using traditional and deep learning techniques. This paper aims to review past work that has been done particularly in the deep learning domain and discusses future directions in the automated detection of cervical cancer. It is believed that this will ensure proper diagnosis and could potentially reduce the prevalence of cervical cancer.
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