Abstract
Dental images segmentation helps to find important regions of a dental X-ray image: tooth isolation. A challenge presents itself in how to detect dental images for correct matching/classification. Edge-based methods have a significant potential role in image segmentation field. These edge based methods use edges to detect objects, boundaries, and other relevant information in an image. This research proposes a deep learning approach to include edge-based features. Firstly, the proposed technique employs data augmentation to address the limited number of dental images, and improvement of accuracy in the evaluation process. Secondly, the edge-based features are extracted using canny edge detection method. Lastly, the neural network features of the (Keras model), from the Keras tool package will be used for converging iterations of the segmentation process, and further classification. The proposed deep learning technique which combines image augmentation, and edge-based features, achieved a higher accuracy of \(89\%\) for both precision, and recall values.
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Majanga, V., Viriri, S. (2020). A Deep Learning Approach for Automatic Segmentation of Dental Images. In: B. R., P., Thenkanidiyoor, V., Prasath, R., Vanga, O. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2019. Lecture Notes in Computer Science(), vol 11987. Springer, Cham. https://doi.org/10.1007/978-3-030-66187-8_14
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DOI: https://doi.org/10.1007/978-3-030-66187-8_14
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