Assessment of deep convolutional neural network models for species identification of forensically-important fly maggots based on images of posterior spiracles.

ABSTRACT: Forensic entomology is the branch of forensic science that is related to using arthropod specimens found in legal issues. Fly maggots are one of crucial pieces of evidence that can be used for estimating post-mortem intervals worldwide. However, the species-level identification of fly maggots is difficult, time consuming, and requires specialized taxonomic training. In this work, a novel method for the identification of different forensically-important fly species is proposed using convolutional neural networks (CNNs). The data used for the experiment were obtained from a digital camera connected to a compound microscope. We compared the performance of four widely used models that vary in complexity of architecture to evaluate tradeoffs in accuracy and speed for species classification incl

SUBMITTER: Apasrawirote D 

PROVIDER: S-EPMC8934339 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature