Fusion extracted features from deep learning for identification of multiple positioning errors in dental panoramic imaging

CONCLUSIONS: This study demonstrates that six SVM classifiers effectively identify multiple positioning errors in dental panoramic imaging. The fusion of extracted image features and the employment of SVM classifiers improve diagnostic precision, suggesting potential enhancements in dental imaging efficiency and diagnostic accuracy. Future research should consider larger datasets and explore real-time clinical application.PMID:37840464 | DOI:10.3233/XST-230171

Source: Journal of X-Ray Science and Technology - October 16, 2023 Category: Radiology Authors: Hsin-Yueh Su Shang-Ting Hsieh Kun-Zhe Tsai Yu-Li Wang Chi-Yuan Wang Shih-Yen Hsu Kuo-Ying Liu Yung-Hui Huang Ya-Wen Wei Nan-Han Lu Tai-Been Chen Source Type: research