Automated identification of Chagas disease vectors using AlexNet pre-trained convolutional neural networks.
Journal:
Medical and veterinary entomology
PMID:
39670626
Abstract
The 158 bug species that make up the subfamily Triatominae are the potential vectors of Trypanosoma cruzi, the etiological agent of Chagas disease. Despite recent progress in developing a picture-based automated system for identification of triatomines, an extensive and diverse image database is required for a broadly useful automated application for identifying these vectors. We evaluated performance of a deep-learning network (AlexNet) for identifying triatomine species from a database of dorsal images of adult insects. We used a sample of photos of 6397 triatomines belonging to seven genera and 65 species from 27 countries. AlexNet had an accuracy of ~0.93 (95% confidence interval [CI], 0.91-0.94) for identifying triatomine species from pictures of varying resolutions. Highest specific accuracy was observed for 21 species in the genera Rhodnius and Panstrongylus. AlexNet performance improved to ~0.95 (95% CI, 0.93-0.96) when only the species with highest vectorial capacity were considered. These results show that AlexNet, when trained with a large, diverse, and well-structured picture set, exhibits excellent performance for identifying triatomine species. This study contributed to the development of an automated Chagas disease vector identification system.