Deep Learning Models for Classification of Dental Diseases Using Orthopantomography X-ray OPG Images
The teeth are the most challenging material to work with in the human body. Existing methods for detecting teeth problems are characterised by low efficiency, the complexity of the experiential operation, and a higher level of user intervention. Older oral disease detection approaches were manual, t...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-09-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/19/7370 |
| _version_ | 1797476941335363584 |
|---|---|
| author | Yassir Edrees Almalki Amsa Imam Din Muhammad Ramzan Muhammad Irfan Khalid Mahmood Aamir Abdullah Almalki Saud Alotaibi Ghada Alaglan Hassan A Alshamrani Saifur Rahman |
| author_facet | Yassir Edrees Almalki Amsa Imam Din Muhammad Ramzan Muhammad Irfan Khalid Mahmood Aamir Abdullah Almalki Saud Alotaibi Ghada Alaglan Hassan A Alshamrani Saifur Rahman |
| author_sort | Yassir Edrees Almalki |
| collection | DOAJ |
| description | The teeth are the most challenging material to work with in the human body. Existing methods for detecting teeth problems are characterised by low efficiency, the complexity of the experiential operation, and a higher level of user intervention. Older oral disease detection approaches were manual, time-consuming, and required a dentist to examine and evaluate the disease. To address these concerns, we propose a novel approach for detecting and classifying the four most common teeth problems: cavities, root canals, dental crowns, and broken-down root canals, based on the deep learning model. In this study, we apply the YOLOv3 deep learning model to develop an automated tool capable of diagnosing and classifying dental abnormalities, such as dental panoramic X-ray images (OPG). Due to the lack of dental disease datasets, we created the Dental X-rays dataset to detect and classify these diseases. The size of datasets used after augmentation was 1200 images. The dataset comprises dental panoramic images with dental disorders such as cavities, root canals, BDR, dental crowns, and so on. The dataset was divided into 70% training and 30% testing images. The trained model YOLOv3 was evaluated on test images after training. The experiments demonstrated that the proposed model achieved 99.33% accuracy and performed better than the existing state-of-the-art models in terms of accuracy and universality if we used our datasets on other models. |
| first_indexed | 2024-03-09T21:10:54Z |
| format | Article |
| id | doaj.art-34a6b2c5f000480e95c4cf89f4fb77df |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:10:54Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-34a6b2c5f000480e95c4cf89f4fb77df2023-11-23T21:48:13ZengMDPI AGSensors1424-82202022-09-012219737010.3390/s22197370Deep Learning Models for Classification of Dental Diseases Using Orthopantomography X-ray OPG ImagesYassir Edrees Almalki0Amsa Imam Din1Muhammad Ramzan2Muhammad Irfan3Khalid Mahmood Aamir4Abdullah Almalki5Saud Alotaibi6Ghada Alaglan7Hassan A Alshamrani8Saifur Rahman9Division of Radiology, Department of Internal Medicine, Medical College, Najran University, Najran 61441, Saudi ArabiaDepartment of Computer Science and Information Technology, University of Sargodha, Sargodha 40100, PakistanDepartment of Computer Science and Information Technology, University of Sargodha, Sargodha 40100, PakistanElectrical Engineering Department, College of Engineering, Najran University Saudi Arabia, Najran 61441, Saudi ArabiaDepartment of Computer Science and Information Technology, University of Sargodha, Sargodha 40100, PakistanDepartment of Preventive Dental Sciences, College of Dentistry, Majmaah University, Al-Majmaah 11952, Saudi ArabiaDepartment of Preventive Dental Sciences, College of Dentistry, Majmaah University, Al-Majmaah 11952, Saudi ArabiaDepartment of Orthodontics and Pediatric Dentistry, College of Dentistry, Qassim University, Buraidah 51452, Saudi ArabiaRadiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran 61441, Saudi ArabiaElectrical Engineering Department, College of Engineering, Najran University Saudi Arabia, Najran 61441, Saudi ArabiaThe teeth are the most challenging material to work with in the human body. Existing methods for detecting teeth problems are characterised by low efficiency, the complexity of the experiential operation, and a higher level of user intervention. Older oral disease detection approaches were manual, time-consuming, and required a dentist to examine and evaluate the disease. To address these concerns, we propose a novel approach for detecting and classifying the four most common teeth problems: cavities, root canals, dental crowns, and broken-down root canals, based on the deep learning model. In this study, we apply the YOLOv3 deep learning model to develop an automated tool capable of diagnosing and classifying dental abnormalities, such as dental panoramic X-ray images (OPG). Due to the lack of dental disease datasets, we created the Dental X-rays dataset to detect and classify these diseases. The size of datasets used after augmentation was 1200 images. The dataset comprises dental panoramic images with dental disorders such as cavities, root canals, BDR, dental crowns, and so on. The dataset was divided into 70% training and 30% testing images. The trained model YOLOv3 was evaluated on test images after training. The experiments demonstrated that the proposed model achieved 99.33% accuracy and performed better than the existing state-of-the-art models in terms of accuracy and universality if we used our datasets on other models.https://www.mdpi.com/1424-8220/22/19/7370BDRdeep learningOPGYOLOdentistryannotation |
| spellingShingle | Yassir Edrees Almalki Amsa Imam Din Muhammad Ramzan Muhammad Irfan Khalid Mahmood Aamir Abdullah Almalki Saud Alotaibi Ghada Alaglan Hassan A Alshamrani Saifur Rahman Deep Learning Models for Classification of Dental Diseases Using Orthopantomography X-ray OPG Images Sensors BDR deep learning OPG YOLO dentistry annotation |
| title | Deep Learning Models for Classification of Dental Diseases Using Orthopantomography X-ray OPG Images |
| title_full | Deep Learning Models for Classification of Dental Diseases Using Orthopantomography X-ray OPG Images |
| title_fullStr | Deep Learning Models for Classification of Dental Diseases Using Orthopantomography X-ray OPG Images |
| title_full_unstemmed | Deep Learning Models for Classification of Dental Diseases Using Orthopantomography X-ray OPG Images |
| title_short | Deep Learning Models for Classification of Dental Diseases Using Orthopantomography X-ray OPG Images |
| title_sort | deep learning models for classification of dental diseases using orthopantomography x ray opg images |
| topic | BDR deep learning OPG YOLO dentistry annotation |
| url | https://www.mdpi.com/1424-8220/22/19/7370 |
| work_keys_str_mv | AT yassiredreesalmalki deeplearningmodelsforclassificationofdentaldiseasesusingorthopantomographyxrayopgimages AT amsaimamdin deeplearningmodelsforclassificationofdentaldiseasesusingorthopantomographyxrayopgimages AT muhammadramzan deeplearningmodelsforclassificationofdentaldiseasesusingorthopantomographyxrayopgimages AT muhammadirfan deeplearningmodelsforclassificationofdentaldiseasesusingorthopantomographyxrayopgimages AT khalidmahmoodaamir deeplearningmodelsforclassificationofdentaldiseasesusingorthopantomographyxrayopgimages AT abdullahalmalki deeplearningmodelsforclassificationofdentaldiseasesusingorthopantomographyxrayopgimages AT saudalotaibi deeplearningmodelsforclassificationofdentaldiseasesusingorthopantomographyxrayopgimages AT ghadaalaglan deeplearningmodelsforclassificationofdentaldiseasesusingorthopantomographyxrayopgimages AT hassanaalshamrani deeplearningmodelsforclassificationofdentaldiseasesusingorthopantomographyxrayopgimages AT saifurrahman deeplearningmodelsforclassificationofdentaldiseasesusingorthopantomographyxrayopgimages |