@article{article_1760369, title={AUTOMATIC CLASSIFICATION SYSTEM FOR PERIAPICAL LESIONS WITH THE TOOTHNET CNN ARCHITECTURE INTEGRATING THE PROPOSED HYBRID ACTIVATION FUNCTION ON CBCT SCANS}, journal={Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi}, volume={29}, pages={75–93}, year={2026}, DOI={10.17780/ksujes.1760369}, url={https://izlik.org/JA74CS79GD}, author={Akalın, Fatma and Özkan, Yasin}, keywords={CBCT görüntüleme yaklaşımı, Orijinal görüntü işleme yöntemi, ToothNet CNN mimarisi, Önerilen Hibrit Aktivasyon Fonksiyonu, normal ve lezyon görüntülerinin sınıflandırılması}, abstract={Imaging techniques are widely used in dentistry to understand the 3D structure of teeth and detect diseases, but their interpretation is time-consuming and prone to error. To address this, decision support systems are increasingly utilized. This study proposes a CNN-based classification model using the UFPE dataset, which includes Cone Beam Computed Tomography (CBCT) scans. In the first scenario, both real and enhanced images were input into a CNN, yielding 68.92% accuracy for enhanced images. Due to a result, enhanced images were used in all other scenarios. In the second scenario, a newly designed CNN architecture called ToothNet, incorporating a custom activation function, was tested. It achieved 69.92% accuracy, 61.45% recall, 62.67% precision, and 68.68% F1-score, showing a 1.45% increase in accuracy. To evaluate generalizability, three more classification scenarios were examined using the same dataset. ToothNet achieved 80.14% accuracy in the “healthy vs. large lesion” and 68.73% in the “healthy vs. small lesion” classification. These results indicate that the proposed architecture not only improves accuracy but is also generalizable across different lesion sizes.}, number={1}