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 suitable for training and testing convolutional neural network (CNN)-based models for medical image classification tasks, particularly in low-resource settings. The images are provided in standard formats (e.g., PNG or JPEG), along with corresponding labels in structured files (e.g., CSV or JSON). This resource is intended to facilitate research in deep learning for lung ultrasound analysis and contribute to more accessible and robust diagnostic tools in global health.
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Institutions
- Makerere University
Categories
Funders
- Makerere UniversityUganda