A Deep Learning Approach for Classification of Dentinal Tubule Occlusions
Yazarlar (4)
Dr. Öğr. Üyesi Anday DURU Karabük Üniversitesi, Türkiye
Prof. Dr. İsmail Rakıp KARAŞ Karabük Üniversitesi, Türkiye
Doç. Dr. Fatih Karayürek Karabük Üniversitesi, Türkiye
Aydın Gülses
Universitätsklinikum Schleswig-Holstein Campus Kiel, Türkiye
Universitätsklinikum Schleswig-Holstein Campus Kiel, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Applied Artificial Intelligence (Q2) | ||
| Dergi ISSN | 0883-9514 | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 06-2022 |
| Cilt / Sayı / Sayfa | 36 / 1 / 2584–2600 | DOI | 10.1080/08839514.2022.2094446 |
| Makale Linki | http://dx.doi.org/10.1080/08839514.2022.2094446 | ||
| UAK Araştırma Alanları |
Bilgi Sistemleri
Görüntü İşleme
Yapay Zeka
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| Özet |
| This study aimed to develop a novel deep learning model for reliable quantification of dentinal tubule occlusions instead of manual assessment techniques, and the performance of the model was compared to other methods in the literature. Ninety-six dentin samples were cut and prepared with desensitizing agents to occlude dentinal tubules on different levels. After obtaining images via scanning electron microscope (SEM), 2793 single dentinal tubule images with 48 × 48 resolution were segmented and labeled. Data augmentation techniques were applied for improvement in the learning rate. The augmented data having a total of 10700 images belonging to five classes were used as the network training dataset. The proposed convolutional neural network (CNN) is a class of deep learning model and was able to classify the degree of dentinal tubule occlusions into five classes with an overall accuracy rate of 90 … |
| Anahtar Kelimeler |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 2 |
| Scopus | 4 |
| Google Scholar | 5 |