| Abstrakt: |
Abnormalities commonly encountered in dental practice include tooth and supporting tissue issues such as caries, periapical abnormalities, resorption, and impacted third molars. Panoramic radiographs are frequently used for image scanning in dentistry and oral surgery. Diagnosing dental anomalies can be time-consuming due to the complexity of the orthodontic area, potentially leading to inaccuracies. This research proposes an end-to-end automated detection of dental and supporting tissue anomalies in patients, encompassing cavities, periapical lesions, resorption, and impacted third molars. This study evaluated the effectiveness of employing various pre-trained Convolutional Neural Network architectures, including ResNet-50, ResNeXt-50 32×4d, Inception-V3, and EfficientNet-V2. To enhance model performance, a batch normalization technique was integrated into the classification layer of these pre-trained models. Data pre-processing techniques, including horizontal and vertical flips, as well as random affine transformations, were applied to augment the dataset. Additionally, an image normalization procedure was implemented before the training and prediction phases. In the evaluation on 202 images, the integrated ResNeXt-50 32x4d model with batch normalization achieved the highest accuracy, precision, recall, and F1-score of 83.663%, 81.615%, 81.271%, and 81.066%, respectively. Based on the F1-score, this model demonstrates promising predictions of tooth and supporting tissue anomalies in an imbalanced dataset. [ABSTRACT FROM AUTHOR] |
