Deep-learning based fusion of spatial relationship classification between mandibular third molar and inferior alveolar nerve using panoramic radiograph images
Nida Kumbasar , Mustafa Taha Güller , Özkan Miloğlu , Emin Argun Oral , Ibrahim Yucel Ozbek
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引用次数: 0
Abstract
It is crucial for clinicians to have a prior knowledge of spatial relationship between impacted mandibular third molar tooth (MM3) and inferior alveolar nerve (IAN) before an extraction procedure. This relationship may exist in four spatial forms in terms of IAN position relative to MM3 although it has not been studied extensively. To identify such relationship type, on the other hand, this study proposes a novel four-class classification framework utilizing fusion of AlexNet, VGG16, VGG19 deep learning methods using panoramic radiograph (PR) images. For this purpose, 546 PR images of impacted MM3, collected from 290 patients, were labeled by specialists using corresponding cone beam computed tomography (CBCT) images. The proposed network is trained and tested using 10 folds cross validation. Experimental studies were performed in different categories. In the first (MM3 and IAN are related/unrelated) an accuracy rate of 94.1% was obtained. In the following IAN resides on the lingual or vestibule (buccal) side of MM3 classification problem, test result of 80.6% accuracy was obtained. Finally, in the challenging four-class classification problem that includes unrelated, lingual, vestibule and other classes, an accuracy rate of 79.7% was achieved. Obtained results show that the proposed method not only presents state-of-the-art results but also suggests a new classification basis for the existing MM3-IAN relationship problem.
期刊介绍:
Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management.
Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.