Dental Impression Tray Selection From Maxillary Arch Images Using Multi-Feature Fusion and Ensemble Classifier
| Autor: | Norli Anida Abdullah, Muhammad Asif Hasan, Mohd Yamani Idna Idris, Omar Tawfiq, Mohammad Mustaneer Rahman |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
Computer science Feature extraction ensemble classifier computer vision Classifier (linguistics) Selection (linguistics) medicine automation in dentistry General Materials Science dental arch image Maxillary arch multi-feature fusion business.industry Deep learning Dental impression tray General Engineering Pattern recognition Object (computer science) Dental arch medicine.anatomical_structure Dental impression Tray Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering business lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 9, Pp 30573-30586 (2021) |
| ISSN: | 2169-3536 |
| Popis: | Dental impression tray is frequently used in dentistry to record the patient’s oral structure for clinical oral diagnosis and treatment planning. Manual procedure of taking impressions is costly, time-consuming, and additionally, no research has been done on selecting dental impression tray from dental arch images using computer vision in real-life scenarios. In this spirit, an intelligent model is proposed based on computer vision and machine learning to select appropriate dental impression trays from maxillary arch images. A dataset of 52 patients’ maxillary arch images have been acquired and various sets of features such as colors, textures, and shapes of the images were extracted to better characterize the maxillary arch images. Considering the importance of the features in describing the maxillary arch object and to improve the classification performance, a method based on multi-feature fusion with ensemble classifier is proposed. Besides, the performance of a deep learning based multilayer perceptron neural network is also investigated. The proposed multi-feature fusion with ensemble classifier attained 92.31% precision, 91.75% recall, 91.75% accuracy, respectively, on the dataset, which clearly establishes the feasibility of the proposed model. An illustration of a real-life application of the proposed model is also provided. |
| Databáze: | OpenAIRE |
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