Automated identification of Chagas disease vectors using AlexNet pre-trained convolutional neural networks

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.