Single and Clustered Cervical Cell Classification with Ensemble and Deep Learning Methods
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Single and Clustered Cervical Cell Classification with Ensemble and Deep Learning Methods Mohammed Kuko 1
&
Mohammad Pourhomayoun 1
# Springer Science+Business Media, LLC, part of Springer Nature 2020
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
1 Introduction Cervical cancer if detected early has an upward of 89% survival rate Mendivil et al. (2016). 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 and time 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. Mendivil et al. (2016); Hewitt et al. (2004). Visually scanning and classifying
* Mohammed Kuko [email protected] * Mohammad Pourhomayoun [email protected] 1
Computer Science Department, California State University, Los Angeles, CA, USA
thousands of cervical cells and cell clusters is a mentally taxing procedure. This process proves to be time-consuming and shown high levels of variability and inaccuracy Solomon et al. (2002). The total number of women who can get accurate and possibly lifesaving results from the Pap smear test is currently bottle necked by the availability and workload of already ov
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