Self-CephaloNet: A Two-stage Novel Framework using Operational Neural Network for Cephalometric Analysis
Journal:
arXiv
Published Date:
Jan 19, 2025
Abstract
Cephalometric analysis is essential for the diagnosis and treatment planning
of orthodontics. In lateral cephalograms, however, the manual detection of
anatomical landmarks is a time-consuming procedure. Deep learning solutions
hold the potential to address the time constraints associated with certain
tasks; however, concerns regarding their performance have been observed. To
address this critical issue, we proposed an end-to-end cascaded deep learning
framework (Self-CepahloNet) for the task, which demonstrated benchmark
performance over the ISBI 2015 dataset in predicting 19 dental landmarks. Due
to their adaptive nodal capabilities, Self-ONN (self-operational neural
networks) demonstrate superior learning performance for complex feature spaces
over conventional convolutional neural networks. To leverage this attribute, we
introduced a novel self-bottleneck in the HRNetV2 (High Resolution Network)
backbone, which has exhibited benchmark performance on the ISBI 2015 dataset
for the dental landmark detection task. Our first-stage results surpassed
previous studies, showcasing the efficacy of our singular end-to-end deep
learning model, which achieved a remarkable 70.95% success rate in detecting
cephalometric landmarks within a 2mm range for the Test1 and Test2 datasets.
Moreover, the second stage significantly improved overall performance, yielding
an impressive 82.25% average success rate for the datasets above within the
same 2mm distance. Furthermore, external validation was conducted using the PKU
cephalogram dataset. Our model demonstrated a commendable success rate of
75.95% within the 2mm range.
