An enhanced deep learning model for detection and classification of dental caries in panoramic radiographs
Yazarlar (7)
Arş. Gör. Dilara Özdemir Karabük Üniversitesi, Türkiye
Dr. Öğr. Üyesi Ahmet KARAOĞLU Sinop Üniversitesi, Türkiye
Caner Özcan Karabük Üniversitesi, Türkiye
Adem Pekince Karabük Üniversitesi, Türkiye
Prof. Dr. Yasin Yaşa Ordu Üniversitesi, Türkiye
Buse Yaren Kazangirler Karabük Üniversitesi, Türkiye
Arş. Gör. Elif Meşeci Zonguldak Bülent Ecevit Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SCOPUS dergilerinde yayınlanan tam makale)
|
||
| Dergi Adı | Neural Computing and Applications | ||
| Dergi ISSN | 0941-0643 Dergi Bilgileri (2026) | ||
| Dergi Tarandığı Indeksler | Scopus | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2026 |
| Kabul Tarihi | 12-11-2025 | Yayınlanma Tarihi | 01-01-2026 |
| Cilt / Sayı / Sayfa | 38 / 1 / – | DOI | 10.1007/s00521-025-11730-4 |
| Makale Linki | https://doi.org/10.1007/s00521-025-11730-4 | ||
| UAK Araştırma Alanları |
Yapay Zeka
Görüntü İşleme
Makine Öğrenmesi
|
||
| Özet |
| Early diagnosis of dental caries has become increasingly important in recent years. It reduces irreversible tooth loss, treatment costs and treatment time. However, since the examination of dental caries is carried out visually by experts on radiographic images, the analysis process is quite exhausting for the experts. In addition, visual analysis may miss early-stage caries due to the workload in the clinical environment. In this study, an automatic caries diagnosis system is proposed to support the expert and to reduce the clinical workload by using panoramic images. The proposed DenseNet121-C model, based on deep learning models, generates results with its configured classifier for caries detection. The dataset prepared for the study includes 14498 tooth images automatically cropped from panoramic images. The proposed model achieved the highest performance on the test set with 93.17% accuracy, 89.43 … |
| Anahtar Kelimeler |
| Deep learning | Dental caries diagnosis | Medical images | Transfer learning |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 4 |
| Google Scholar | 6 |