Phytophagous, blood-suckers or predators? Automated identification of Chagas disease vectors and similar bugs using convolutional neural network algorithms

Abstract

Correct identification of blood-sucking bugs, such as triatomines, is important because they are vectors of Chagas' disease. Identifying these insects is often difficult for non-specialists. Deep learning is emerging as a solution for automated identification. This study evaluates the performance of three convolutional neural networks (CNNs) - AlexNet, MobileNetV2 and ResNet-50 - to identify bugs categorized by their feeding habits: 'blood-suckers', 'phytophagous' and 'predators'. A dataset of 707 dorsal view pictures was divided into training, validation, and test subsets (70 %, 10 %, and 20 %, respectively). Transfer learning was used to train the models, and Grad-CAM visualizations identified the picture regions that most influenced the predictions. All models achieved an accuracy of over 94 %, with ResNet-50 slightly outperforming the other models in terms of sensitivity and specificity. ROC and AUC analyses confirmed the reliability of these algorithms, highlighting their potential for robust bug identification. This study demonstrates the applicability of CNNs in distinguishing Triatominae from other insects, paving the way for the development of affordable vector identification tools to improve Chagas disease surveillance and control.