Advanced cervical cancer classification: enhancing pap smear images with hybrid PMD Filter-CLAHE

TL;DR

This paper demonstrates that a hybrid PMD filter-CLAHE preprocessing technique significantly improves CNN-based cervical cancer classification accuracy on Pap smear images.

Contribution

It introduces a novel hybrid image preprocessing method combining PMD filter and CLAHE to enhance CNN performance in cervical cancer detection.

Findings

Hybrid preprocessing improves accuracy by up to 13.62%

Enhanced images lead to better precision and recall

CNN models perform better with the proposed preprocessing

Abstract

Cervical cancer remains a significant health problem, especially in developing countries. Early detection is critical for effective treatment. Convolutional neural networks (CNN) have shown promise in automated cervical cancer screening, but their performance depends on Pap smear image quality. This study investigates the impact of various image preprocessing techniques on CNN performance for cervical cancer classification using the SIPaKMeD dataset. Three preprocessing techniques were evaluated: perona-malik diffusion (PMD) filter for noise reduction, contrast-limited adaptive histogram equalization (CLAHE) for image contrast enhancement, and the proposed hybrid PMD filter-CLAHE approach. The enhanced image datasets were evaluated on pretrained models, such as ResNet-34, ResNet-50, SqueezeNet-1.0, MobileNet-V2, EfficientNet-B0, EfficientNet-B1, DenseNet-121, and DenseNet-201. The results show that hybrid preprocessing PMD filter-CLAHE can improve the Pap smear image quality and CNN architecture performance compared to the original images. The maximum metric improvements are 13.62% for accuracy, 10.04% for precision, 13.08% for recall, and 14.34% for F1-score. The proposed hybrid PMD filter-CLAHE technique offers a new perspective in improving cervical cancer classification performance using CNN architectures.