# Hybrid Dense-UNet201 Optimization for Pap Smear Image Segmentation Using Spider Monkey Optimization

arXiv: https://arxiv.org/abs/2504.12807 · HTML: https://mlsanity.com/paper/2504.12807/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 37 references that are in the map.
- First posted on arXiv 17 Apr 2025.

## Builds on (most cited first, 5 of 5)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Medical Image Segmentation Review: The success of U-Net](https://mlsanity.com/paper/2211.14830/) (2022)
- [New Insight in Cervical Cancer Diagnosis Using Convolution Neural Network Architecture](https://mlsanity.com/paper/2410.17735/) (2024)
- [Optimized Pap Smear Image Enhancement: Hybrid PMD Filter-CLAHE Using Spider Monkey Optimization](https://mlsanity.com/paper/2502.15156/) (2025)

## Abstract (arXiv)

Pap smear image segmentation is crucial for cervical cancer diagnosis. However, traditional segmentation models often struggle with complex cellular structures and variations in pap smear images. This study proposes a hybrid Dense-UNet201 optimization approach that integrates a pretrained DenseNet201 as the encoder for the U-Net architecture and optimizes it using the spider monkey optimization (SMO) algorithm. The Dense-UNet201 model excelled at feature extraction. The SMO was modified to handle categorical and discrete parameters. The SIPaKMeD dataset was used in this study and evaluated using key performance metrics, including loss, accuracy, Intersection over Union (IoU), and Dice coefficient. The experimental results showed that Dense-UNet201 outperformed U-Net, Res-UNet50, and Efficient-UNetB0. SMO Dense-UNet201 achieved a segmentation accuracy of 96.16%, an IoU of 91.63%, and a Dice coefficient score of 95.63%. These findings underscore the effectiveness of image preprocessing, pretrained models, and metaheuristic optimization in improving medical image analysis and provide new insights into cervical cell segmentation methods.