MLMI-2024 (Mosquito Larvae Microscopic Images)

Published: 12 September 2024| Version 1 | DOI: 10.17632/pgby6jmtp4.1
Contributors:
Rizka Wakhidatus Sholikah, Afrida Rohmatin Nuriyah, Annisaa Sri Indrawanti

Description

The MLMI-2024 dataset comprises microscopic images of mosquito larvae captured using a microscope and a smartphone camera. It features two species: Aedes aegypti and Culex quinquefasciatus. For each species, the dataset includes images of the full body, head, 8th abdominal segment, and siphon. The dataset is organized into two main folders—one for Aedes aegypti and the other for Culex quinquefasciatus—with each species folder containing four subfolders labeled "full body," "head," "abdomen," and "siphon." The dataset consists of unaugmented original images, with 100 images in each subfolder. The images are 3,456 x 3,456 pixels, captured using a 64MP smartphone camera and a microscope.

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Institutions

  • Institut Teknologi Sepuluh Nopember
  • Universitas Airlangga Institute of Tropical Disease

Categories

Entomology, Computer Vision, Machine Learning, Larva, Mosquito, Deep Learning

Funders

  • Research Grant from Information Technology Departmen, Institut Teknologi Sepuluh Nopember
    Grant ID: 2097/PKS/ITS/2024

Licence