Supervised Learning Computer Vision Benchmark for Snake Species Identification From Photographs: Implications for Herpetology and Global Health.

Autor:	Durso AM; Department of Biological Sciences, Florida Gulf Coast University, Ft. Myers, FL, United States.; Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland., Moorthy GK; Eloop Mobility Solutions, Chennai, India., Mohanty SP; AICrowd, Lausanne, Switzerland., Bolon I; Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland., Salathé M; AICrowd, Lausanne, Switzerland.; Digital Epidemiology Laboratory, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland., Ruiz de Castañeda R; Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
Jazyk:	angličtina
Zdroj:	Frontiers in artificial intelligence [Front Artif Intell] 2021 Apr 20; Vol. 4, pp. 582110. Date of Electronic Publication: 2021 Apr 20 (Print Publication: 2021).
DOI:	10.3389/frai.2021.582110
Abstrakt:	We trained a computer vision algorithm to identify 45 species of snakes from photos and compared its performance to that of humans. Both human and algorithm performance is substantially better than randomly guessing (null probability of guessing correctly given 45 classes = 2.2%). Some species (e.g., Boa constrictor ) are routinely identified with ease by both algorithm and humans, whereas other groups of species (e.g., uniform green snakes, blotched brown snakes) are routinely confused. A species complex with largely molecular species delimitation (North American ratsnakes) was the most challenging for computer vision. Humans had an edge at identifying images of poor quality or with visual artifacts. With future improvement, computer vision could play a larger role in snakebite epidemiology, particularly when combined with information about geographic location and input from human experts. Competing Interests: Authors SM and MS are CEO and co-founders of AICrowd, which was used in the research and sponsors this call for manuscripts. Author GM was employed by the company Eloop Mobility Solutions. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor declared a past co-authorship with one of the authors SH. (Copyright © 2021 Durso, Moorthy, Mohanty, Bolon, Salathé and Ruiz de Castañeda.)
Databáze:	MEDLINE
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