Morphological classification of hematophagous Diptera with Convolutional Neural Networks: A mapping of literature

Abstract

This review analyzes studies that primarily address the morphological classification of hematophagous Diptera, with limited mention of other insects. These networks have become increasingly important in morphological analysis through the accurate and efficient automatic identification of species, surpassing even traditional methods based on human observation. The main architectures used, such as VGG-16, YOLOv5, Faster R-CNN, Mask R-CNN, ResNet, and Swin Transformer-L, are reviewed, highlighting their applications in the detection and identification of different anatomical parts. Common limitations are also mentioned, such as the need for large volumes of classified data and variability in image quality. Finally, current trends are identified that point to the development of more robust hybrid models capable of recognizing new species and improving accuracy under real-world conditions. This literature mapping provides greater certainty and evidence regarding the most important identification methods in the field of entomology. These findings highlight the gap in literature related to the availability of public data, parameters used, data volume, image quality, and model evaluation, providing a solid foundation to guide future research in the field of entomology.