Classification of DNA damages on segmented comet assay images using convolutional neural network

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dc.authoridATILA, UMIT/0000-0002-1576-9977
dc.authoridBaydilli, Yusuf Yargi/0000-0002-4457-2081
dc.contributor.authorAtila, Umit
dc.contributor.authorBaydilli, Yusuf Yargi
dc.contributor.authorSehirli, Eftal
dc.contributor.authorTuran, Muhammed Kamil
dc.date.accessioned2024-09-29T15:55:09Z
dc.date.available2024-09-29T15:55:09Z
dc.date.issued2020
dc.departmentKarabük Üniversitesien_US
dc.description.abstractBackground and Objective: Identification and quantification of DNA damage is a very significant subject in biomedical research area which still needs more robust and effective methods. One of the cheapest, easy to use and most successful method for DNA damage analyses is comet assay. In this study, performance of Convolutional Neural Network was examined on quantification of DNA damage using comet assay images and was compared to other methods in the literature. Methods: 796 single comet grayscale images with 170 x 170 resolution labeled by an expert and classified into 4 classes each having approximately 200 samples as G0 (healthy), G1 (poorly defective), G2 (defective) and G3 (very defective) were utilized. 120 samples were used as test dataset and the rest were used in data augmentation process to achieve better performance with training of Convolutional Neural Network. The augmented data having a total of 9995 images belonging to four classes were used as network training data set. Results: The proposed model, which was not dependent to pre-processing parameters of image processing for DNA damage classification, was able to classify comet images into 4 classes with an overall accuracy rate of 96.1%. Conclusions: This paper primarily focuses on features and usage of Convolutional Neural Network as a novel method to classify comet objects on segmented comet assay images. (C) 2019 Elsevier B.V. All rights reserved.en_US
dc.description.sponsorshipResearch Fund of the Karabuk University [KBU-BAP-16/2-DR-102]en_US
dc.description.sponsorshipThis work was supported by Research Fund of the Karabuk University, Project Number: KBU-BAP-16/2-DR-102. Our thanks to Karabuk University to provide an opportunity and work cooperatively during comet assay experiment.en_US
dc.identifier.doi10.1016/j.cmpb.2019.105192
dc.identifier.issn0169-2607
dc.identifier.issn1872-7565
dc.identifier.pmid31733518en_US
dc.identifier.scopus2-s2.0-85074768788en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.cmpb.2019.105192
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4499
dc.identifier.volume186en_US
dc.identifier.wosWOS:000517795500003en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherElsevier Ireland Ltden_US
dc.relation.ispartofComputer Methods and Programs in Biomedicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectComet assayen_US
dc.subjectDNA damageen_US
dc.subjectConvolutional neural Networken_US
dc.subjectDeep learningen_US
dc.titleClassification of DNA damages on segmented comet assay images using convolutional neural networken_US
dc.typeArticleen_US

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