Machine learning in the field of dentistry
Oral disease is very common, both old and young will be troubled by it. For more severe and complex cases, dentists often use dental radiographs to diagnose and plan treatment. However, the use of dental radiographs for auxiliary diagnosis can only rely on the experience of doctors, and dental radio...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/158196 |
| _version_ | 1811676484696276992 |
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| author | Li, Hengliang |
| author2 | Muhammad Faeyz Karim |
| author_facet | Muhammad Faeyz Karim Li, Hengliang |
| author_sort | Li, Hengliang |
| collection | NTU |
| description | Oral disease is very common, both old and young will be troubled by it. For more severe and complex cases, dentists often use dental radiographs to diagnose and plan treatment. However, the use of dental radiographs for auxiliary diagnosis can only rely on the experience of doctors, and dental radiographs for the naked eye is complicated, long-time work will lead to human fatigue and misjudgment.
The development of machine learning makes object detection achieve an efficient and high accuracy performance. Therefore, machine learning related technologies have been applied in the field of dentistry. The RetinaNet chosen in this project is an one-stage algorithm which achieves the accuracy comparable to two-stage algorithm while retaining the advantages of one-stage algorithm with few memory consumption and fast processing speed.
The main purpose of this project is to demonstrate RetinaNet can be applied to dentistry by constructing a RetinaNet for the detection of dental crowns in dental radiographs, and to use a trained model to detect dental crowns. The algorithm was built by Python, and finally verified to achieve high precision in dental dataset. {mAP}_{80} reaches 89.80%, which is higher than the accuracy of the network on VOC2007 dataset. |
| first_indexed | 2024-10-01T02:22:13Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/158196 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:22:13Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1581962023-07-07T19:29:35Z Machine learning in the field of dentistry Li, Hengliang Muhammad Faeyz Karim School of Electrical and Electronic Engineering faeyz@ntu.edu.sg Engineering::Electrical and electronic engineering Oral disease is very common, both old and young will be troubled by it. For more severe and complex cases, dentists often use dental radiographs to diagnose and plan treatment. However, the use of dental radiographs for auxiliary diagnosis can only rely on the experience of doctors, and dental radiographs for the naked eye is complicated, long-time work will lead to human fatigue and misjudgment. The development of machine learning makes object detection achieve an efficient and high accuracy performance. Therefore, machine learning related technologies have been applied in the field of dentistry. The RetinaNet chosen in this project is an one-stage algorithm which achieves the accuracy comparable to two-stage algorithm while retaining the advantages of one-stage algorithm with few memory consumption and fast processing speed. The main purpose of this project is to demonstrate RetinaNet can be applied to dentistry by constructing a RetinaNet for the detection of dental crowns in dental radiographs, and to use a trained model to detect dental crowns. The algorithm was built by Python, and finally verified to achieve high precision in dental dataset. {mAP}_{80} reaches 89.80%, which is higher than the accuracy of the network on VOC2007 dataset. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-31T12:55:39Z 2022-05-31T12:55:39Z 2022 Final Year Project (FYP) Li, H. (2022). Machine learning in the field of dentistry. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158196 https://hdl.handle.net/10356/158196 en W3355-212 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Li, Hengliang Machine learning in the field of dentistry |
| title | Machine learning in the field of dentistry |
| title_full | Machine learning in the field of dentistry |
| title_fullStr | Machine learning in the field of dentistry |
| title_full_unstemmed | Machine learning in the field of dentistry |
| title_short | Machine learning in the field of dentistry |
| title_sort | machine learning in the field of dentistry |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/158196 |
| work_keys_str_mv | AT lihengliang machinelearninginthefieldofdentistry |