Intraoral radiograph anatomical region classification using neural networks

ConclusionsAccording to our findings, automated classification of anatomical classes in digital intraoral radiographs is feasible with an expected top-1 classification accuracy of almost 90%, even for images with significant distortions or overlapping anatomy. Model architecture, data augmentation strategies, the use of pooling and normalization layers as well as model capacity were identified as the factors most contributing to classification performance.
Source: International Journal of Computer Assisted Radiology and Surgery - Category: Intensive Care Source Type: research