Fractional Perona–Malik-based processing for noise reduction and structure preservation in red, green, blue Pap smear images

Abstract

Cervical cancer screening relies heavily on Pap smear analysis, yet image noise, low contrast, and overlapping cellular structures continue to limit diagnostic accuracy and the performance of automated systems. This study introduces a fractional Perona–Malik diffusion (FPMD) framework that extends the classical anisotropic diffusion model using fractional-order operators to achieve more flexible, edge-sensitive smoothing. The method is applied to red, green, blue (RGB) Pap smear images and benchmarked against classical PMD and conventional filters using entropy, blind/referenceless image spatial quality evaluator (BRISQUE), and edge preservation index (EPI). FPMD yields substantial improvements, achieving the lowest BRISQUE score (18.88) and the highest EPI values (>0.92) across all channels, indicating superior structural preservation and perceptual quality. While classical PMD produces slightly higher entropy, it introduces artifacts that degrade visual realism. FPMD provides a more controlled enhancement, producing diagnostically meaningful contrast and clearer cytological boundaries. These results highlight its potential as a robust preprocessing tool for both manual assessment and artificial intelligence (AI)-assisted cervical cancer screening.