Single and Clustered Cervical Cell Classification with Ensemble and Deep Learning Methods

Mohammed Kuko () and Mohammad Pourhomayoun ()
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Mohammed Kuko: California State University
Mohammad Pourhomayoun: California State University

Information Systems Frontiers, 2020, vol. 22, issue 5, No 4, 1039-1051

Abstract: Abstract 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.

Keywords: Cervical cancer; Cervical cytology; Pap smear; Liquid-based cytology; Machine vision; Machine learning; Unsupervised clustering; Ensemble learning; Deep learning; Convolutional neural networks (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s10796-020-10028-1

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