Contrast Enhancement or Noise Reduction? On Improving Cervical Cancer Classification - Ach Khozaimi; Ulfatun Nahdhiyah (Universitas Negeri Semarang, 2026)
Purpose: To evaluate the impact of image preprocessing techniques, specifically contrast enhancement and noise reduction, on improving the CNN performance on Pap smear image classification for early cervical cancer detection. Methods: Three CNN architectures (ResNet34, DenseNet121, and MobileNet-V2) were trained and evaluated on the SIPaKMeD dataset. Two preprocessing techniques were applied: Contrast Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement and Perona-Malik Diffusion (PMD) filter for noise reduction. Model performance was assessed using a confusion matrix. Results: Preprocessing improved classification performance across all models. CLAHE significantly increased the accuracy of ResNet34 from 76.73% to 84.16% and DenseNet121 from 83.17% to 84.16%, while also providing modest improvement for MobileNet-V2. In contrast, PMD filtering yielded limited improvement and, in some cases, slightly reduced model performance. Novelty: This study provides a systematic comparison of contrast enhancement and noise reduction techniques across multiple CNN architectures. This study demonstrates that contrast enhancement is more effective than noise reduction in improving CNN performance. The study provides new pipelines for improving cervical cancer classification.