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

Classification of the Cervical Vertebrae Maturation (CVM) stages Using the Tripod Network