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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 performances have been observed. To address this critical issue, we propose an end-to-end cascaded deep learning framework (Self-CephaloNet) for the task, which demonstrates benchmark performance over the ISBI 2015 dataset in predicting 19 cephalometric landmarks. Due to their adaptive nodal capabilities, Self-ONN (self-operational neural networks) demonstrates superior learning performance for complex feature spaces over conventional convolutional neural networks. To leverage this attribute, we introduce a novel self-bottleneck in the HRNetV2 (high-resolution network) backbone, which has exhibited benchmark performance on our landmark detection task. Our first-stage result surpasses previous studies, showcasing the efficacy of our singular end-to-end deep learning model, which achieves a remarkable 70.95% success rate in detecting cephalometric landmarks within a 2-mm range for the Test1 and Test2 datasets\u00a0which are part of ISBI 2015 dataset. Moreover, the second stage significantly improves overall performance, yielding an impressive 82.25% average success rate for the datasets above within the same 2-mm distance. Furthermore, external validation has been conducted using the PKU cephalogram dataset. Our model demonstrates a commendable success rate of 75.95% within the 2-mm range.<\/jats:p>","DOI":"10.1007\/s00521-025-11097-6","type":"journal-article","created":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T18:33:57Z","timestamp":1741372437000},"page":"9777-9805","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Self-CephaloNet: a two-stage novel framework using operational neural network for cephalometric analysis"],"prefix":"10.1007","volume":"37","author":[{"given":"Md. 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