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

Author

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  • Mohammed Kuko

    (California State University)

  • Mohammad Pourhomayoun

    (California State University)

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.

Suggested Citation

  • Mohammed Kuko & Mohammad Pourhomayoun, 2020. "Single and Clustered Cervical Cell Classification with Ensemble and Deep Learning Methods," Information Systems Frontiers, Springer, vol. 22(5), pages 1039-1051, October.
    • Handle: RePEc:spr:infosf:v:22:y:2020:i:5:d:10.1007_s10796-020-10028-1
    DOI: 10.1007/s10796-020-10028-1
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    Cited by:

    1. R. Elakkiya & Pandi Vijayakumar & Marimuthu Karuppiah, 2021. "COVID_SCREENET: COVID-19 Screening in Chest Radiography Images Using Deep Transfer Stacking," Information Systems Frontiers, Springer, vol. 23(6), pages 1369-1383, December.
    2. Lydia Bouzar-Benlabiod & Stuart H. Rubin, 2020. "Heuristic Acquisition for Data Science," Information Systems Frontiers, Springer, vol. 22(5), pages 1001-1007, October.
    3. Yoon Sang Lee & Chulhwan Chris Bang, 2022. "Framework for the Classification of Imbalanced Structured Data Using Under-sampling and Convolutional Neural Network," Information Systems Frontiers, Springer, vol. 24(6), pages 1795-1809, December.
    4. A. Geethapriya & S. Valli, 2021. "An Enhanced Approach to Map Domain-Specific Words in Cross-Domain Sentiment Analysis," Information Systems Frontiers, Springer, vol. 23(3), pages 791-805, June.
    5. Harleen Kaur & Shafqat Ul Ahsaan & Bhavya Alankar & Victor Chang, 2021. "A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets," Information Systems Frontiers, Springer, vol. 23(6), pages 1417-1429, December.

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