A Hybrid Transfer Learning Approach to Teeth Diagnosis Using Orthopantomogram Radiographs
The rise in the emphasis on oral diseases has elevated the need to automate the diagnostic process of such diseases. Fortunately, the availability of modern computing devices has made the automated diagnosis of teeth readily possible using deep learning. Despite this, concerns about the accuracy and...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10770196/ |
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| author | Ahmed Alabd-Aljabar Zain Raisan Mohammed Adnan Salam Dhou |
| author_facet | Ahmed Alabd-Aljabar Zain Raisan Mohammed Adnan Salam Dhou |
| author_sort | Ahmed Alabd-Aljabar |
| collection | DOAJ |
| description | The rise in the emphasis on oral diseases has elevated the need to automate the diagnostic process of such diseases. Fortunately, the availability of modern computing devices has made the automated diagnosis of teeth readily possible using deep learning. Despite this, concerns about the accuracy and function of automated diagnosis remain among patients. To showcase the performance of such algorithms, we propose two approaches for the task of teeth diagnosis utilizing Orthopantomograms (panoramic radiographs): 1) a direct classification approach; and 2) a hybrid approach that combines a deep learning model with a traditional classifier. The results revealed that all ten chosen deep learning models experienced a similar or improved performance when used in conjunction with a machine learning classifier. In particular, Vision Transformer (ViT) performed the best with a record accuracy of 96% using both the direct and hybrid approaches. However, the hybrid framework combining AlexNet with a Support Vector Machine achieved an accuracy of 94%, and although it falls short of ViT in terms of performance, it comprises far fewer parameters. This highlights the approach’s effectiveness in improving performance without the need to use a deeper model, making it well-suited for clinical adoption where efficiency is important. |
| format | Article |
| id | doaj-art-2a8436bfd67740959c9bd9e2ba83746a |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-2a8436bfd67740959c9bd9e2ba83746a2025-08-20T01:54:38ZengIEEEIEEE Access2169-35362024-01-011217814217815210.1109/ACCESS.2024.350792510770196A Hybrid Transfer Learning Approach to Teeth Diagnosis Using Orthopantomogram RadiographsAhmed Alabd-Aljabar0https://orcid.org/0009-0004-3701-2002Zain Raisan1https://orcid.org/0009-0002-6699-1321Mohammed Adnan2https://orcid.org/0009-0001-4440-4934Salam Dhou3https://orcid.org/0000-0002-8143-6417Department of Computer Science and Engineering, American University of Sharjah, Sharjah, United Arab EmiratesDepartment of Computer Science and Engineering, American University of Sharjah, Sharjah, United Arab EmiratesDepartment of Computer Science and Engineering, American University of Sharjah, Sharjah, United Arab EmiratesDepartment of Computer Science and Engineering, American University of Sharjah, Sharjah, United Arab EmiratesThe rise in the emphasis on oral diseases has elevated the need to automate the diagnostic process of such diseases. Fortunately, the availability of modern computing devices has made the automated diagnosis of teeth readily possible using deep learning. Despite this, concerns about the accuracy and function of automated diagnosis remain among patients. To showcase the performance of such algorithms, we propose two approaches for the task of teeth diagnosis utilizing Orthopantomograms (panoramic radiographs): 1) a direct classification approach; and 2) a hybrid approach that combines a deep learning model with a traditional classifier. The results revealed that all ten chosen deep learning models experienced a similar or improved performance when used in conjunction with a machine learning classifier. In particular, Vision Transformer (ViT) performed the best with a record accuracy of 96% using both the direct and hybrid approaches. However, the hybrid framework combining AlexNet with a Support Vector Machine achieved an accuracy of 94%, and although it falls short of ViT in terms of performance, it comprises far fewer parameters. This highlights the approach’s effectiveness in improving performance without the need to use a deeper model, making it well-suited for clinical adoption where efficiency is important.https://ieeexplore.ieee.org/document/10770196/Dental imagingdental informaticsdeep learningmachine learningorthopantomographytransfer learning |
| spellingShingle | Ahmed Alabd-Aljabar Zain Raisan Mohammed Adnan Salam Dhou A Hybrid Transfer Learning Approach to Teeth Diagnosis Using Orthopantomogram Radiographs IEEE Access Dental imaging dental informatics deep learning machine learning orthopantomography transfer learning |
| title | A Hybrid Transfer Learning Approach to Teeth Diagnosis Using Orthopantomogram Radiographs |
| title_full | A Hybrid Transfer Learning Approach to Teeth Diagnosis Using Orthopantomogram Radiographs |
| title_fullStr | A Hybrid Transfer Learning Approach to Teeth Diagnosis Using Orthopantomogram Radiographs |
| title_full_unstemmed | A Hybrid Transfer Learning Approach to Teeth Diagnosis Using Orthopantomogram Radiographs |
| title_short | A Hybrid Transfer Learning Approach to Teeth Diagnosis Using Orthopantomogram Radiographs |
| title_sort | hybrid transfer learning approach to teeth diagnosis using orthopantomogram radiographs |
| topic | Dental imaging dental informatics deep learning machine learning orthopantomography transfer learning |
| url | https://ieeexplore.ieee.org/document/10770196/ |
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