Ahmet Karaoglu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Ahmet Karaoglu has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Adem Pekince
Department of Oral and Maxillofacial Radiology, Karabuk University, Karabuk, Turkey
1 shared publication - Buse Yaren Kazangirler
Institute of Graduate Programs, Department of Computer Engineering, Karabuk University, Karabuk, Turkey
1 shared publication - Caner Ozcan
Department of Software Engineering, Karabuk University, Karabuk, Turkey
1 shared publication - Dilara Ozdemir
Institute of Graduate Programs, Department of Computer Engineering, Karabuk University, Karabuk, Turkey
1 shared publication - Elif Meseci
Department of Computer Engineering, Zonguldak Bülent Ecevit University, Zonguldak, Turkey
1 shared publication - Yasin Yasa
Faculty of Dentistry, Ordu University, Ordu 52200, Turkey
1 shared publication
Latest Publications
- Top Cited PapersOpen Access
- 14 January 2026
- Published by Springer Nature in Neural Computing and Applications
- Vol. 38 (1) , 3
An enhanced deep learning model for detection and classification of dental caries in panoramic radiographs
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% precision, 85.84% recall, and 87.49% F1-score. Considering the high results of the current study, dentists can spend more time on treatment during dental examinations, thanks to the model’s ability to distinguish between caries and non-caries teeth. The results obtained were compared with the Mask R-CNN results. In addition, the performance of the deep learning architectures was investigated on an unbalanced dataset.
- 14 January 2026
- Published by Springer Nature in Neural Computing and Applications
- Vol. 38 (1) , 3
Top Cited PapersOpen AccessAn enhanced deep learning model for detection and classification of dental caries in panoramic radiographs
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% precision, 85.84% recall, and 87.49% F1-score. Considering the high results of the current study, dentists can spend more time on treatment during dental examinations, thanks to the model’s ability to distinguish between caries and non-caries teeth. The results obtained were compared with the Mask R-CNN results. In addition, the performance of the deep learning architectures was investigated on an unbalanced dataset.