Snake Species Identification Challenge
Snakebite is the most deadly neglected tropical disease (NTD), being responsible for a dramatic humanitarian crisis in global health
Snakebite causes over 100,000 human deaths and 400,000 victims of disability and disfigurement globally every year. It affects poor and rural communities in developing countries, which host the highest venomous snake diversity and the highest burden of snakebite due to limited medical expertise and access to antivenoms
Antivenoms can be life‐saving when correctly administered but this depends first on the correct taxonomic identification (i.e. family, genus, species) of the biting snake. Snake identification is challenging due to:
- their high diversity
- the incomplete or misleading information provided by snakebite victims
- the lack of knowledge or resources in herpetology that healthcare professionals have
In this challenge we want to explore how Machine Learning can help with snake identification, in order to potentially reduce erroneous and delayed healthcare actions.