A Hybrid CNN-Extreme Learning Machine with Augmented Dataset for DNA Damage Classification Using Comet Assay from Buccal Mucosa Sample
Penulis/Author
YUES TADRIK HAFIYAN (1); Afiahayati, S.Kom., M.Cs., Ph.D (2); drg. Ryna Dwi Yanuaryska, Ph.D (3); Edgar Anarossi (4); VINCENT MICHAEL S (5); JOKO TRIYANTO (6); Prof. Yasubumi Sakakibara, Ph.D. (7)
Tanggal/Date
29 2021
Kata Kunci/Keyword
Abstrak/Abstract
DNA is the information carrier in cells that are susceptible to damage, either 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 cellcultured, 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%
Rumpun Ilmu
Ilmu Komputer
Bahasa Asli/Original Language
English
Level
Internasional
Status
Dokumen Karya
No
Judul
Tipe Dokumen
Aksi
1
ijicic-Yues Tadrik Hafiyan et al_August 2021.pdf
[PAK] Full Dokumen
2
A HYBRID CONVOLUTIONAL NEURAL NETWORK-EXTREME LEARNING MACHINE WITH AUGMENTED DATASET FOR DNA DAMAGE CLASSIFICATION USING COMET ASSAY FROM BUCCAL MUCOSA SAMPLE LEARNING MACHINE WITH AUGMENTED DATASET
