A Systematic Review of Effective Data Augmentation in Cervical Cancer Detection

A Systematic Review of Effective Data Augmentation in Cervical Cancer Detection

Authors

  • Betelhem Wubineh Wroclaw University of Science and Technology, Faculty of Information and Communication Technology,
  • Andrzej Rusiecki Wroclaw University of Science and Technology, Faculty of Information and Communication Technology, https://orcid.org/0000-0003-2239-1076
  • Krzysztof Halawa Wroclaw University of Science and Technology, Faculty of Information and Communication Technology, https://orcid.org/0000-0001-6508-0468

Abstract

The rapid progress of AI has made computer-assisted systems essential in medical fields like cervical cytology analysis. Deep learning requires large datasets, but data scarcity and privacy concerns pose challenges. Data augmentation addresses this by generating additional images and improving model accuracy and generalizability. This review examines effective augmentation techniques and top-performing deep-learning models for segmentation and classification in cervical cancer detection. Analyzing 57 articles, we found that hybrid deep feature fusion with augmentation (rotation, flipping, shifting, brightness adjustments) achieved 99.8% accuracy in binary and 99.1% in multiclass classification. Augmentation is vital for enhancing model performance in limited data scenarios.

Additional Files

Published

2025-05-30

Issue

Vol. 71 No. 2 (2025)

Section

Applied Informatics

License

Copyright (c) 2025 International Journal of Electronics and Telecommunication

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