Using a multi-head, convolutional neural network with data augmentation to improve electropherogram classification performance

Research output: Contribution to journalArticlepeer-review

Abstract

DNA profiles are generated in forensic biology laboratories around the world. It is possible that these profiles are assessed by two independent people in order for the profiles to be ‘read’. Recent work has been carried out to develop a neural network model to classify fluorescence in a DNA profile electropherogram and potentially replace one, or both human readers. The ability to use neural networks for this function has been programmed into the software FaSTR™ DNA, which has been validated for use in at least one laboratory in Australia. The work that previously developed a neural network system had a number of limitations, specifically it was computer intensive, did not make the best use of available data, and consequently the performance of this model was sub-optimal in some conditions (particularly for low-intensity peaks). In the current work a new neural network model is developed that makes various improvements on the old model, by using convolutional layers, a multi-head architecture and data augmentation. Results indicate that an improved performance can be expected for low-intensity profiles.

Original languageEnglish
Article number102605
Number of pages12
JournalForensic Science International: Genetics
Volume56
DOIs
Publication statusPublished - Jan 2022

Keywords

  • Convolutional neural network
  • Data augmentation
  • Electropherogram
  • FaSTR DNA

Fingerprint

Dive into the research topics of 'Using a multi-head, convolutional neural network with data augmentation to improve electropherogram classification performance'. Together they form a unique fingerprint.

Cite this