Deep Learning Based Lesion Detection on Dental Panoramic Radiographs

• Published in: 2023 Innovations in Intelligent Systems and Applications Conference (ASYU) pp. 1 - 6
• Main Authors: Demir, Kubra, Aksakalli, Isil Karabey, Baygin, Nursena, Sokmen, Ozlem Comakli
• Format: Conference Proceeding
• Language: English
• Published: IEEE 11.10.2023
• Subjects: Deep learning; Dentistry; lesion detection; Lesions; Radiography; SAM; Sensitivity; Technological innovation; Teeth; YOLOv5; YOLOv8
• ISSN: 2770-7946
• DOI: 10.1109/ASYU58738.2023.10296824

With the development of technology, one of the new breakthroughs in the health field has been dental lesion detection. Dental lesion occurs with tissue changes that occur in the parts of the teeth that are especially decayed or filled, close to the gingiva. In cases where this change is not visible and cannot be distinguished from healthy tissue, computer-aided artificial intelligence methods can be used to assist the dentist quickly and accurately. In this study, panoramic images among radiographic images were obtained and deep learning methods applied to these images were compared in terms of accuracy, sensitivity, precision, F1 score, frame per second and intersection of units metrics. The labels of 660 panoramic images were validated by a specialist dentist and the images were trained using YOLOv5, YOLOv8 and SAM models. The experimental results show that YOLOv8 method reached the highest performance value with 0.975 accuracy, 0.978 precision and 0.986 sensitivity rate in the generated test model.