Cervical cancer if detected early has an upward of 89% survival rate. The leading tool in identifying cervical cancer in its infancy is the Papanicolaou (Pap smear) test, which since its introduction dropped cervical cancer related deaths by 60%. The Pap smear test or Liquid-based Cytology (LBC) is a time-consuming procedure that requires a pathologist to manually identify cervical cells that may be in the middle of the processes that indicate cervical cancer. Unfortunately, due to the expenses related to conducting the Pap smear test many women are blocked from access to it and this leads to over 4000 women dying annually from cervical cancer in the United States alone. The aim of this research is to automate the methods used by pathologists in conducting the Pap smear or LBC. We show that using machine vision, ensemble learning and deep learning methods a significant portion of the Pap smear can be done automated. We set out to extract cells and cell clusters and classify those samples based on the Bethesda System for reporting cervical cytology. Achieving an accuracy of 90.4% and 91.6% for the ensemble learning and deep learning methods respectively when evaluated with a five-fold cross-validation demonstrates promise in the creation of an automated Pap smear screening test.

中文翻译：

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集成和深度学习方法对子宫颈细胞和群集细胞进行分类

如果及早发现宫颈癌，其存活率可达89％以上。识别婴儿期子宫颈癌的主要工具是Papanicolaou（巴氏涂片检查）测试，该测试自引入以来使与宫颈癌有关的死亡人数减少了60％。子宫颈抹片检查或基于液体的细胞学检查（LBC）是一项耗时的过程，需要病理学家手动识别可能处于表明宫颈癌过程中间的宫颈细胞。不幸的是，由于进行子宫颈抹片检查的相关费用，许多妇女无法进行子宫颈抹片检查，仅在美国，每年就有4000多名妇女死于宫颈癌。这项研究的目的是使病理学家进行巴氏涂片或LBC的方法自动化。我们证明了使用机器视觉，集成学习和深度学习方法子宫颈抹片检查的很大一部分可以自动完成。我们着手提取细胞和细胞簇，并根据Bethesda系统对这些样品进行分类，以报告宫颈细胞学。通过五重交叉验证评估时，集成学习和深度学习方法分别达到90.4％和91.6％的准确性，证明了创建自动巴氏涂片筛查测试的希望。