Development of a deep learning based framework for classification of Indian venomous snakes integrated with explainable artificial intelligence for primary and emergency care providers
Ikhlaas Ifthikar Abusayeed Manna, Usha Wagle, Badhrinarayanan Balaji, Vrinda Lath, Niranjana Sampathila, Sudhakara Upadya P, Freston Marc Sirur, Wuelton Monteiro, Wuelton Monteiro, Wuelton Monteiro, Wuelton Monteiro · 2026
Read Development of a deep learning based framework for classification of Indian venomous snakes integrated with explainable artificial intelligence for primary and emergency care providers by Ikhlaas Ifthikar Abusayeed Manna, Usha Wagle, Badhrinarayanan Balaji, Vrinda Lath, Niranjana Sampathila, Sudhakara Upadya P, Freston Marc Sirur, Wuelton Monteiro, Wuelton Monteiro, Wuelton Monteiro, Wuelton Monteiro (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13271475.txt.
Description
Ikhlaas Ifthikar Abusayeed Manna and coauthors present in 2026 a deep learning system that sorts Indian snakes into venomous and non-venomous groups from photos. India carries the world's largest share of snakebite deaths, and rural clinics often lack experts who can name a snake quickly. Fast, correct naming guides antivenom use and urgent care.
The team trained four image models on pictures of 20 medically important Indian species under matched conditions. A visual check method confirmed the models judged head shape and body markings rather than background detail. Linked to a web page, the system aims to support frontline and emergency staff where specialist help is scarce.
Catalog record
| CREATOR | Ikhlaas Ifthikar Abusayeed Manna, Usha Wagle, Badhrinarayanan Balaji, Vrinda Lath, Niranjana Sampathila, Sudhakara Upadya P, Freston Marc Sirur, Wuelton Monteiro, Wuelton Monteiro, Wuelton Monteiro, Wuelton Monteiro |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 71 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |