  We present a novel deep learning method for fully automated detection and
classification of the Cervical Vertebrae Maturation (CVM) stages. The deep
convolutional neural network consists of three parallel networks (TriPodNet)
independently trained with different initialization parameters. They also have
a built-in set of novel directional filters that highlight the Cervical Verte
edges in X-ray images. Outputs of the three parallel networks are combined
using a fully connected layer. 1018 cephalometric radiographs were labeled,
divided by gender, and classified according to the CVM stages. Resulting
images, using different training techniques and patches, were used to train
TripodNet together with a set of tunable directional edge enhancers. Data
augmentation is implemented to avoid overfitting. TripodNet achieves the
state-of-the-art accuracy of 81.18\% in female patients and 75.32\% in male
patients. The proposed TripodNet achieves a higher accuracy in our dataset than
the Swin Transformers and the previous network models that we investigated for
CVM stage estimation.
