Deep Learning Based Cervical Cancer Detection

Authors

  • U Muruganantham
  • N Palanivel
  • E.Benny Mathew
  • A.Jerald arul
  • M. Kaalasthiyan
  • D.Dhinesh rajan

Keywords:

HSIL (High-grade Squamous Intraepithelial Lesion), LSIL (Low-grade Squamous Intraepithelial Lesion), Squamous Cell Carcinoma (SCC), Neural Networks for Medical Imaging, Machine Learning in Healthcare, Computer-Aided Diagnosis (CAD), Transfer Learning

Abstract

Cervical cancer remains a significant global health concern, ranking among the leading causes of cancer-related deaths in women. Early detection through advanced screening techniques can drastically improve survival rates. This study presents a deep learning-based approach utilizing EfficientNet B0, a powerful yet lightweight convolutional neural network (CNN), to classify cervical cancer stages based on histopathological images. The model categorizes images into four classes: HSIL, LSIL, Normal (NL), and SCC, ensuring precise and reliable classification. The trained model is integrated into a Flask web application, allowing users to upload cervical cell images for real-time diagnosis. Upon classification, the application provides stage-specific medical guidance to support early intervention. The system enhances cervical cancer detection by offering an automated, accessible, and efficient diagnostic tool. Future enhancements may include real-time image processing and integration with telemedicine platforms to broaden its clinical applications.

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Published

2025-05-21

How to Cite

1.
Muruganantham U, Palanivel N, Mathew E, arul A, Kaalasthiyan M, rajan D. Deep Learning Based Cervical Cancer Detection. J Neonatal Surg [Internet]. 2025 May 21 [cited 2026 May 25];14(25S):816-2. Available from: https://jneonatalsurg.com/index.php/jns/article/view/6212

Issue

Vol. 14 No. 25S (2025): Journal of Neonatal Surgery

Section

Original Article

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