基于蜘蛛猴优化的混合Dense-UNet201优化用于巴氏涂片图像分割
Hybrid Dense-UNet201 Optimization for Pap Smear Image Segmentation Using Spider Monkey Optimization
AI总结：
针对巴氏涂片图像分割中传统模型处理复杂细胞结构的难题，提出将预训练DenseNet201作为U-Net编码器并通过改进的蜘蛛猴优化（SMO）算法优化的混合Dense-UNet201方法，在SIPaKMeD数据集上取得96.16%准确率等优异结果，为宫颈癌诊断提供更高效的分割方案。
摘要：
巴氏涂片图像分割对宫颈癌诊断至关重要，但传统分割模型常难以处理巴氏涂片图像中复杂的细胞结构和变化。本研究提出一种混合Dense-UNet201优化方法，将预训练的DenseNet201作为U-Net架构的编码器，并使用蜘蛛猴优化（SMO）算法对其进行优化。Dense-UNet201模型在特征提取方面表现出色，SMO被修改以处理分类和离散参数。研究使用SIPaKMeD数据集，通过损失、准确率、交并比（IoU）和Dice系数等关键性能指标评估。实验结果显示，Dense-UNet201优于U-Net、Res-UNet50和Efficient-UNetB0，SMO优化的Dense-UNet201实现了96.16%的分割准确率、91.63%的IoU和95.63%的Dice系数。这些发现强调了图像预处理、预训练模型和元启发式优化在改进医学图像分析中的有效性，为宫颈细胞分割方法提供了新见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
