Lacuna Malaria Detection Challenge Dataset

Dataset Overview

• Total Images: Up to 40 images per blood slide, capturing a diverse range of data.
• Image Capture: Smartphone images taken by placing the phone over the microscope to capture the Field of View (FOV) of the blood slide.
• Microscope Data: Each image includes critical information, such as the objective lens and stage micrometer readings, for proper analysis.
• Source: This dataset complements existing malaria microscopy datasets and focuses on low-resource settings like Uganda.

Key Features:

• High-Quality Images: Smartphone-captured images offering detailed views suitable for AI-powered malaria detection.
• Comprehensive Annotations: Images come with important metadata, including microscope settings, for better context.
• Designed for Low-Resource Settings: Specifically intended for regions with limited access to advanced medical equipment, making it easier to apply machine learning models in such environments.
• Generalizable Data: While focused on Uganda, the dataset aims to improve the generalization of machine learning models for other regions facing similar challenges.

Applications:

• Malaria Diagnosis: Perfect for training Computer Vision models to quickly and accurately identify malaria from blood slide images.
• AI Model Development: Assists researchers in developing machine learning models that work in diverse regions with varying data conditions.
• Public Health Research: Supports studies aiming to advance malaria detection and improve public health technologies for low-resource environments.

Data Processing:

• • Field of View (FOV): Images are captured by positioning the smartphone camera directly over the microscope, ensuring precise and consistent FOV images.
  • Metadata: Alongside each image, the stage micrometer readings and objective lens settings are included to provide valuable context.
  • Consistency: A maximum of 40 images per blood slide ensures a diverse dataset for training accurate machine learning models, reducing model biases.