Classification of DNA damages on segmented comet assay images using convolutional neural network
Yazarlar (4)
Doç. Dr. Ümit Atila Karabük Üniversitesi, Türkiye
Dr. Öğr. Üyesi Yusuf Yargı Baydilli Karabük Üniversitesi, Türkiye
Doç. Dr. Eftal ŞEHİRLİ Karabük Üniversitesi, Türkiye
Doç. Dr. Muhammed Kamil TURAN Karabük Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Computer Methods and Programs in Biomedicine (Q1) | ||
| Dergi ISSN | 0169-2607 | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 04-2020 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 01-04-2020 |
| Cilt / Sayı / Sayfa | 186 / 1 / 105192–0 | DOI | 10.1016/j.cmpb.2019.105192 |
| Makale Linki | http://dx.doi.org/10.1016/j.cmpb.2019.105192 | ||
| UAK Araştırma Alanları |
Makine Öğrenmesi
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| Özet |
| Background and ObjectiveIdentification 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.Methods796 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 … |
| Anahtar Kelimeler |
| Comet assay | Convolutional neural Network | Deep learning | DNA damage |
Science Direct
Classification of DNA damages on segmented comet assay images using convolutional neural network
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 20 |
| Scopus | 25 |
| Google Scholar | 42 |