A Dataset of Lung Ultrasound Images for Automated AI-based Lung Disease Classification
Description
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.
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Funding
Swedish International Development Cooperation Agency
Makerere University
International Development Research Centre