A hybrid convolutional neural network-extreme learning machine with augmented dataset for dna damage classification using comet assay from buccal mucosa sample

Yues Tadrik Hafiyan, Afiahayati, Ryna Dwi Yanuaryska, Edgar Anarossi, Vincent Michael Sutanto, Joko Triyanto, Yasubumi Sakakibara

Research output: Contribution to journalArticlepeer-review

Abstract

DNA is the information carrier in cells that are susceptible to damage, ei-ther naturally or due to external influences. Comet assays are often used by experts to determine the level of damage. However, the comet assays gathered with swab technique (Buccal Mucosa for example) often produced a higher noise level compared to ones that are cell-cultured, thus, making the analysis process more difficult. In this research, we proposed a novel way to assess the degree of damage from Buccal Mucosa comet assays using a hybrid of Convolutional Neural Network (CNN) and Extreme Learning Machine (ELM). The CNN was used to capture and extract spatial relation from every comet, while the ELM was used as a classifier that can minimize the risk of vanishing gradient. Our hybrid CNN-ELM model scored 96.96% for accuracy, while the VGG16-ELM scored 88.4% and ResNet50-ELM 76.8%.

Original languageEnglish
Pages (from-to)1191-11201
Number of pages10011
JournalInternational Journal of Innovative Computing, Information and Control
Volume17
Issue number4
DOIs
Publication statusPublished - 2021

Keywords

  • Buccal Mucosa
  • Comet assay
  • Convolutional neural network
  • Extreme learning machine

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Information Systems
  • Computational Theory and Mathematics

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