DCA-CUSAT明场显微痰涂片结核病数据集
包含101张ZN染色痰涂片明场显微图像，用于结核病杆菌检测的公开数据集，助力自动化诊断。
基本信息
资源简介
DCA-CUSAT明场显微痰涂片结核病数据集
【数据集背景】
本数据集源站标题为《arXiv Query: search_query=&id_list=2501.03538v1&start=0&max_results=1》。源站首次发布：2025-01-07。
【数据内容】
源站原始描述：Tuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis, continues to be a major global health threat despite being preventable and curable. This burden is particularly high in low and middle income countries. Microscopy remains essential for diagnosing TB by enabling direct visualization of Mycobacterium tuberculosis in sputum smear samples, offering a cost effective approach for early detection and effective treatment. Given the labour-intensive nature of microscopy, auto…
【主题与分类】
主题标签：cs.CV、eess.IV。
【适用方向】
影像类数据可用于医学图像分析模型（检测、分割、分类）的训练与评测，也可用于标注质量与成像参数相关性的研究。
【获取与许可】
源站页面：https://arxiv.org/abs/2501.03538v1
下载信息
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使用方式
数据集说明
DCA-CUSAT明场显微痰涂片结核病数据集 对应论文数据集（arXiv 预印本）。
数据获取指引
- 打开论文页面获取作者与项目信息：https://arxiv.org/abs/2501.03538v1
- 论文 Data Availability / Code Availability 章节标注了数据实际托管位置；
- 获取到实际数据链接后，按对应平台标准方式下载。
论文摘要：Abstract:Tuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis, continues to be a major global health threat despite being preventable and curable. This burden is particularly high in low and middle income countries. Microscopy remains essential for diagnosing TB by enabling direct visualization of Mycobacterium tuberculosis in sputum smear samples, offering a cost effective approach for early detection and effective treatment. Given the labour-intensive nature of microscopy, automating the detection of bacilli in microscopic images is crucial to improve both the expediency and reliability of TB diagnosis. The current methodologies for detecting tuberculosis bacilli in bright field microscopic sputum smear images are hindered by limited automation capabilities, inconsistent segmentation quality, and constrained classification precision. This paper proposes a twostage deep learning methodology for tuberculosis bacilli detection, comprising bacilli segmentation followed by classification. In the initial phase, an advanced U-Net model employing attention blocks and residual connections is proposed to segment microscopic sputum smear images, enabling the extraction of Regions of Interest (ROIs). The extracted ROIs are then classified using a Vision Transformer, which we specifically customized as TBViT to enhance the precise detection of bacilli within the images. For the experiments, a newly developed dataset of microscopic sputum smear images derived from Ziehl-Neelsen-stained slides is used in conjunction with existing public datasets. The qualitative
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