Machine learning in the field of dentistry
Due to the increasing availability of dental data, deep learning has been adopted to automate clinical tasks. Object detection using deep learning techniques has gained popularity in the field of dentistry due to the growing demand for automated diagnostic imaging. This study aimed to detect and num...
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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/10356/158916 |
| _version_ | 1826127102593728512 |
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| author | Chia, Ming Hui |
| author2 | Muhammad Faeyz Karim |
| author_facet | Muhammad Faeyz Karim Chia, Ming Hui |
| author_sort | Chia, Ming Hui |
| collection | NTU |
| description | Due to the increasing availability of dental data, deep learning has been adopted to automate clinical tasks. Object detection using deep learning techniques has gained popularity in the field of dentistry due to the growing demand for automated diagnostic imaging. This study aimed to detect and number teeth and dental conditions such as root canal and implant on dental x-rays using a convolutional neural network, which provides fast and accurate results. 700 dental panoramic images were used in this study. Each image and tooth were annotated and categorized manually. Faster R-CNN with backbone network ResNet 101 was selected as it has the best performance at the COCO Object detection contest and is considered a state-of-the-art object detection model. The model was able to number and detect 34 classes (32 teeth, Root Canal, and Implant). It performed well, providing accurate detections with detection scores of more than 90% on test images that are comparable to a dental expert. A Graphical User Interface (GUI) was developed using the python library pyqt5 to allow users to perform analysis with various options using the model. |
| first_indexed | 2024-10-01T07:03:15Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/158916 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:03:15Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1589162023-07-07T19:01:20Z Machine learning in the field of dentistry Chia, Ming Hui Muhammad Faeyz Karim School of Electrical and Electronic Engineering AlviDental faeyz@ntu.edu.sg Engineering::Electrical and electronic engineering Due to the increasing availability of dental data, deep learning has been adopted to automate clinical tasks. Object detection using deep learning techniques has gained popularity in the field of dentistry due to the growing demand for automated diagnostic imaging. This study aimed to detect and number teeth and dental conditions such as root canal and implant on dental x-rays using a convolutional neural network, which provides fast and accurate results. 700 dental panoramic images were used in this study. Each image and tooth were annotated and categorized manually. Faster R-CNN with backbone network ResNet 101 was selected as it has the best performance at the COCO Object detection contest and is considered a state-of-the-art object detection model. The model was able to number and detect 34 classes (32 teeth, Root Canal, and Implant). It performed well, providing accurate detections with detection scores of more than 90% on test images that are comparable to a dental expert. A Graphical User Interface (GUI) was developed using the python library pyqt5 to allow users to perform analysis with various options using the model. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-06-06T08:46:46Z 2022-06-06T08:46:46Z 2022 Final Year Project (FYP) Chia, M. H. (2022). Machine learning in the field of dentistry. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158916 https://hdl.handle.net/10356/158916 en A3171-211 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Chia, Ming Hui 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/10356/158916 |
| work_keys_str_mv | AT chiaminghui machinelearninginthefieldofdentistry |