用改进的蛛猴算法优化密集连接的UNet，提升宫颈涂片图像分割精度。
Hybrid Dense-UNet201 Optimization for Pap Smear Image Segmentation Using Spider Monkey Optimization
- 用预训练DenseNet201做编码器，结合UNet结构提取细胞特征
- 改进蛛猴算法优化模型参数，实现96.16%准确率、91.63% IoU
- 适合医学图像分割研究者，尤其关注宫颈癌筛查与智能诊断
宫颈涂片图像分割对宫颈癌诊断至关重要，但传统模型难以处理复杂细胞结构和图像差异。本文提出一种融合预训练DenseNet201作为编码器的Dense-UNet201，并采用改进的蛛猴优化（SMO）算法进行优化。针对分类与离散参数，对SMO进行了适配性修改。实验在SIPaKMeD数据集上进行，评估指标包括损失、准确率、交并比（IoU）和Dice系数。结果表明，Dense-UNet201优于U-Net、Res-UNet50和Efficient-UNetB0；经SMO优化后，模型达到96.16%准确率、91.63% IoU和95.63% Dice系数。该方法验证了预训练模型与元启发式优化在医学图像分析中的有效性，为宫颈细胞分割提供了新思路。
原文摘要 · Abstract (English)
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.
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