A Dataset of Lung Ultrasound Images for Automated AI-based Lung Disease Classification

This dataset contains a curated benchmark collection of 1,062 labelled lung ultrasound (LUS) images collected from patients at Mulago National Referral Hospital and Kiruddu Referral Hospital in Kampala, Uganda. The images were acquired and annotated by senior radiologists to support the development and evaluation of artificial intelligence (AI) models for pulmonary disease diagnosis. Each image is categorized into one of three classes: Probably COVID-19 (COVID-19), Diseased Lung but Probably Not COVID-19 (Other Lung Disease), and Healthy Lung.

The dataset addresses key challenges in LUS interpretation, including inter-operator variability, low signal-to-noise ratios, and reliance on expert sonographers. It is particularly suitable for training and testing convolutional neural network (CNN)-based models for medical image classification tasks in low-resource settings. The images are provided in standard formats such as PNG or JPEG, with corresponding labels stored in structured files like CSV or JSON to facilitate ease of use in machine learning workflows.

In this second version of the dataset, we have extended the resource by including a folder containing the original unprocessed raw data, as well as the scripts used to process, clean, and sort the data into the final labelled set. These additions promote transparency and reproducibility, allowing researchers to understand the full data pipeline and adapt it for their own applications. This resource is intended to advance research in deep learning for lung ultrasound analysis and to contribute toward building more accessible and reliable diagnostic tools in global health.

本数据集收录了经筛选整理的基准测试合集，包含1062张带标注的肺部超声（Lung Ultrasound, LUS）图像，采集自乌干达坎帕拉的穆拉戈国家转诊医院与基鲁杜转诊医院的患者。图像均由资深放射科医师采集并标注，用于支撑肺部疾病诊断相关人工智能（Artificial Intelligence, AI）模型的开发与评估。每张图像被划分为以下三类之一：疑似新型冠状病毒肺炎（COVID-19）、肺部存在病变但疑似非新冠病毒肺炎（其他肺部疾病），以及健康肺部。

该数据集针对性解决了肺部超声解读中的核心挑战，包括操作者间阅片差异、低信噪比问题，以及对专业超声医师的依赖。其尤其适用于低资源场景下，基于卷积神经网络（Convolutional Neural Network, CNN）的医学图像分类模型的训练与测试。图像以PNG、JPEG等标准格式提供，配套标签存储于CSV、JSON等结构化文件中，可便捷适配各类机器学习工作流。

在本数据集的第二版中，我们新增了包含原始未处理数据的文件夹，以及用于将数据处理、清洗并整理为最终带标注数据集的配套脚本。这些新增内容提升了研究的透明度与可复现性，便于研究者完整理解全数据处理流程，并将其适配至自身研究场景。本数据集旨在推动肺部超声分析领域的深度学习研究，助力开发更具可及性与可靠性的全球卫生诊断工具。