标题： 使用注意力残差U-Net和集成分类的增强结核杆菌检测 显示英文标题

标题： Enhanced Tuberculosis Bacilli Detection using Attention-Residual U-Net and Ensemble Classification

摘要： 结核病（TB）由结核分枝杆菌引起，仍然是一个重要的全球健康问题，需要及时的诊断和治疗。 目前从明亮场显微镜痰涂片图像中检测结核杆菌的方法存在自动化程度低、分割性能不足和分类准确率有限的问题。 本文提出了一种高效的混合方法，结合深度学习进行分割和集成模型进行分类。 引入了一个增强的U-Net模型，结合注意力块和残差连接，以精确分割显微镜痰涂片图像，促进感兴趣区域（ROIs）的提取。 这些ROIs随后使用包含支持向量机（SVM）、随机森林和极端梯度提升（XGBoost）的集成分类器进行分类，从而准确识别图像中的杆菌。 在新创建的数据集以及公共数据集上进行的实验表明，所提出的模型在分割性能、分类准确率和自动化方面优于现有方法。 显示英文摘要

摘要： Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a critical global health issue, necessitating timely diagnosis and treatment. Current methods for detecting tuberculosis bacilli from bright field microscopic sputum smear images suffer from low automation, inadequate segmentation performance, and limited classification accuracy. This paper proposes an efficient hybrid approach that combines deep learning for segmentation and an ensemble model for classification. An enhanced U-Net model incorporating attention blocks and residual connections is introduced to precisely segment microscopic sputum smear images, facilitating the extraction of Regions of Interest (ROIs). These ROIs are subsequently classified using an ensemble classifier comprising Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boost (XGBoost), resulting in an accurate identification of bacilli within the images. Experiments conducted on a newly created dataset, along with public datasets, demonstrate that the proposed model achieves superior segmentation performance, higher classification accuracy, and enhanced automation compared to existing methods.