Segmentation and Classification of Pap Smear Images for Cervical Cancer Detection Using Deep Learning

TL;DR

This paper presents a deep learning framework combining segmentation and classification for Pap smear images to improve cervical cancer detection, demonstrating marginal performance gains with segmentation.

Contribution

It introduces a combined U-Net segmentation and classification model for Pap smear analysis, evaluating the impact of segmentation on diagnostic accuracy.

Findings

Segmentation slightly improved precision and F1-score.

Using segmented images offers a more balanced classification performance.

Impact of segmentation on classification performance is limited.

Abstract

Cervical cancer remains a significant global health concern and a leading cause of cancer-related deaths among women. Early detection through Pap smear tests is essential to reduce mortality rates; however, the manual examination is time consuming and prone to human error. This study proposes a deep learning framework that integrates U-Net for segmentation and a classification model to enhance diagnostic performance. The Herlev Pap Smear Dataset, a publicly available cervical cell dataset, was utilized for training and evaluation. The impact of segmentation on classification performance was evaluated by comparing the model trained on segmented images and another trained on non-segmented images. Experimental results showed that the use of segmented images marginally improved the model performance on precision (about 0.41 percent higher) and F1-score (about 1.30 percent higher), which suggests a slightly more balanced classification performance. While segmentation helps in feature extraction, the results showed that its impact on classification performance appears to be limited. The proposed framework offers a supplemental tool for clinical applications, which may aid pathologists in early diagnosis.