用注意力残差U-Net和集成分类提升结核杆菌显微检测精度
Enhanced Tuberculosis Bacilli Detection using Attention-Residual U-Net and Ensemble Classification
- 引入注意力残差U-Net精准分割痰液涂片中的菌体区域
- 集成SVM、随机森林与XGBoost分类器，准确率达98.7%
- 新数据集与公开数据集均验证其自动化与准确性优势
结核病由结核分枝杆菌引起，仍是重大全球健康问题，亟需及时诊断。现有基于明场显微镜痰液涂片的检测方法存在自动化程度低、分割性能不足、分类准确率有限等问题。本文提出一种高效混合方法：结合深度学习分割与集成分类。设计一种融合注意力模块与残差连接的增强型U-Net模型，实现对显微痰液涂片的精准分割，提取感兴趣区域（ROIs）。随后，采用包含支持向量机（SVM）、随机森林和极端梯度提升（XGBoost）的集成分类器对这些区域进行分类，实现菌体的高精度识别。在自建数据集及公开数据集上的实验表明，该模型在分割性能、分类准确率和自动化程度方面均优于现有方法。
原文摘要 · Abstract (English)
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.
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