Version 1
: Received: 15 July 2024 / Approved: 15 July 2024 / Online: 17 July 2024 (04:44:48 CEST)
How to cite:
Vilcapoma, P.; Parra Meléndez, D.; Fernández, A.; Vásconez, I. N.; Gatica, G.; Vásconez, J. P. Comparison of Faster R-CNN, YOLO, and SSD for Third Molar Angle Detection in Dental Panoramic X-rays. Preprints2024, 2024071272. https://doi.org/10.20944/preprints202407.1272.v1
Vilcapoma, P.; Parra Meléndez, D.; Fernández, A.; Vásconez, I. N.; Gatica, G.; Vásconez, J. P. Comparison of Faster R-CNN, YOLO, and SSD for Third Molar Angle Detection in Dental Panoramic X-rays. Preprints 2024, 2024071272. https://doi.org/10.20944/preprints202407.1272.v1
Vilcapoma, P.; Parra Meléndez, D.; Fernández, A.; Vásconez, I. N.; Gatica, G.; Vásconez, J. P. Comparison of Faster R-CNN, YOLO, and SSD for Third Molar Angle Detection in Dental Panoramic X-rays. Preprints2024, 2024071272. https://doi.org/10.20944/preprints202407.1272.v1
APA Style
Vilcapoma, P., Parra Meléndez, D., Fernández, A., Vásconez, I. N., Gatica, G., & Vásconez, J. P. (2024). Comparison of Faster R-CNN, YOLO, and SSD for Third Molar Angle Detection in Dental Panoramic X-rays. Preprints. https://doi.org/10.20944/preprints202407.1272.v1
Chicago/Turabian Style
Vilcapoma, P., Gustavo Gatica and Juan Pablo Vásconez. 2024 "Comparison of Faster R-CNN, YOLO, and SSD for Third Molar Angle Detection in Dental Panoramic X-rays" Preprints. https://doi.org/10.20944/preprints202407.1272.v1
Abstract
The use of artificial intelligence algorithms (AI) has gained importance for dental applications in recent years. Analyzing using IA information from different sensor data such as images or X-ray radiographs can help to improve medical decisions and to achieve early diagnosis of different dental pathologies. In particular, the use of deep learning (DL) techniques based on convolutional neural networks (CNNs) has obtained promising results in dental applications based on images, in which approaches based on classification, detection, and segmentation are being studied with growing interest. However, there are still several challenges to be tackled, such as the data quality and quantity, the variability among categories, and the analysis of the possible bias and variance associated with each dataset distribution. In this work, we compare three different DL object detection models, which are Faster R-CNN, YOLO V2, and SSD. Each object detection architecture was trained, calibrated, validated, and tested with three different feature extraction CNNs which are ResNet-18, ResNet-50, and ResNet-101, which were the networks that best fit our dataset distribution. Based on such detection networks, we detect four different categories of angles in third molars using X-ray panoramic radiographs by using Winter's classification criterion. This criterion characterizes the third molar's position regarding the second molar's longitudinal axis. The detected categories for the third molars are distoangular, vertical, mesioangular, and horizontal. For training, we used a total of 644 panoramic X-ray images. The results obtained in the testing dataset reached up to 99% mean average accuracy performance, demonstrating the YOLOV2 obtained higher effectiveness in solving the third molar angle detection problem. These results demonstrate that the use of CNNs for object detection in panoramic X-rays represents a promising solution in dental applications.
Keywords
Dentistry; Third Molars Angle Detection; Artificial Intelligence; Convolutional Neural Networks
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
