Deep Learning Based Classification of Cervical Cancer Stages Using Transfer Learning Models

Abstract

Introduction

Cervical cancer is one of the leading causes of mortality among women, emphasizing the need for accurate diagnostic methods particularly in developing countries where access to regular screening is limited. Early detection and accurate classification of cervical cancer stages are crucial for effective treatment and improved survival rates.

Objective

This study explores the potential of deep learning based convolutional neural networks (CNNs) for classifying cervical cytological images from the Sipahan Kanker Metadata (SIPaKMeD) dataset.

Materials and Methods

The SIPaKMeD dataset originally containing 4,049 images is augmented to 24,294 images to enhance model generalization. We employed VGG-16, EfficientNet-B7, and CapsNet CNN models using transfer learning with ImageNet pretrained weights to improve classification accuracy.

Results

The experimental results show that EfficientNet-B7 achieved the average highest classification accuracy of 91.34%, outperforming VGG-16 (86.5%) and CapsNet (81.34%). Evaluation metrics such as precision, recall, and F1-score further validate the robustness of EfficientNet-B7 in distinguishing between different cervical cancer stages. After testing with various hyperparameters, EfficientNet-B7 minimizes misclassification errors and is able to categorize data more accurately compared to other CNN models.

Conclusion

These findings highlight the potential of deep learning CNNs for automated cervical cancer diagnosis, aiding doctors in clinical decision-making to classify medical images and diagnose diseases. Consequently, diagnostic accuracy improves, facilitating more effective treatment planning in the healthcare sector.

Keywords

female - uterine cervical neoplasms - early detection of cancer - deep neural networks - pathologists

Data Availability Statement

Datasets associated with this article are available at (https://universe.roboflow.com/ik-zu-quan-o9tdm/sipakmed-ioflq).

Patient's Consent

Patient consent was waived as the data used were anonymized and obtained from publicly available sources.

Publication History

Article published online:
03 July 2025

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