Self-CephaloNet: A Two-stage Novel Framework using Operational Neural Network for Cephalometric Analysis

• URL: http://arxiv.org/abs/2501.10984v1
• Date: Sun, 19 Jan 2025 08:37:27 GMT
• Title: Self-CephaloNet: A Two-stage Novel Framework using Operational Neural Network for Cephalometric Analysis
• Authors: Md. Shaheenur Islam Sumon, Khandaker Reajul Islam, Tanzila Rafique, Gazi Shamim Hassan, Md. Sakib Abrar Hossain, Kanchon Kanti Podder, Noha Barhom, Faleh Tamimi, Abdulrahman Alqahtani, Muhammad E. H. Chowdhury,
• Abstract summary: We propose an end-to-end cascaded deep learning framework (Self-CepahloNet) for the task. Self-ONN (self-operational neural networks) demonstrate superior learning performance for complex feature spaces. Our model achieved a remarkable 70.95% success rate in detecting cephalometric landmarks within a 2mm range.
• Score: 0.0
• License:
• 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.

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