Lacuna Malaria Detection Challenge Dataset

The dataset contains a total of 3,925 images, including 2,747 images in the training set and 1,178 images in the test set. The images in the dataset were captured by placing a smartphone above a microscope and capturing the field of view (FOV) of the blood smear through the microscope eyepiece. In addition to the image, the slide on which the image was captured, the microscope's stage micrometer reading, and the objective lens settings were also recorded, with a maximum of 40 images captured per slide.

This blood smear image dataset complements the existing malaria microscopy dataset, helps to use computer vision technology to quickly and accurately diagnose malaria in resource-poor settings, and can be used to improve machine learning models.