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

IF 1.3 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Innovative Computing Information and Control Pub Date : 2021-01-01 DOI:10.24507/IJICIC.17.04.1191
Yues Tadrik Hafiyan, Afiahayati, Ryna Dwi Yanuaryska, Edgar Anarossi, V. Sutanto, J. Triyanto, Y. Sakakibara
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引用次数: 3

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 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%.
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基于增强数据集的混合卷积神经网络-极限学习机用于口腔黏膜样本的彗星分析dna损伤分类
DNA是细胞中的信息载体,容易受到自然或外部影响的损害。专家经常使用彗星测定法来确定损伤程度。然而,与细胞培养相比,用拭子技术收集的彗星分析(例如,颊粘膜)通常产生更高的噪声水平,从而使分析过程更加困难。在这项研究中,我们提出了一种使用卷积神经网络(CNN)和极限学习机(ELM)的混合方法来评估口腔黏膜彗星检测损伤程度的新方法。使用CNN捕获和提取每颗彗星的空间关系,而使用ELM作为分类器,可以最小化梯度消失的风险。我们的混合CNN-ELM模型的准确率为96.96%,而VGG16-ELM的准确率为88.4%,ResNet50-ELM的准确率为76.8%。
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来源期刊
CiteScore
3.20
自引率
20.00%
发文量
0
审稿时长
4.3 months
期刊介绍: The primary aim of the International Journal of Innovative Computing, Information and Control (IJICIC) is to publish high-quality papers of new developments and trends, novel techniques and approaches, innovative methodologies and technologies on the theory and applications of intelligent systems, information and control. The IJICIC is a peer-reviewed English language journal and is published bimonthly
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