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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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , |
| التنسيق: | article |
| منشور في: |
2024
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/11073/26274 |
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| _version_ | 1864513432967970816 |
|---|---|
| author | Alabd-Aljabar, Ahmed |
| author2 | Raisan, Zain Adnan, Mohammed Dhou, Salam |
| author2_role | author author author |
| author_facet | Alabd-Aljabar, Ahmed Raisan, Zain Adnan, Mohammed Dhou, Salam |
| author_role | author |
| dc.creator.none.fl_str_mv | Alabd-Aljabar, Ahmed Raisan, Zain Adnan, Mohammed Dhou, Salam |
| dc.date.none.fl_str_mv | 2024-11-28 2025-08-21T09:29:40Z 2025-08-21T09:29:40Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Alabd-Aljabar, A., Raisan, Z., Adnan, M., & Dhou, S. (2024). A Hybrid Transfer Learning Approach to Teeth Diagnosis Using Orthopantomogram Radiographs. IEEE Access, 12, 178142–178152. https://doi.org/10.1109/access.2024.3507925 2169-3536 https://hdl.handle.net/11073/26274 10.1109/access.2024.3507925 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.relation.none.fl_str_mv | https://doi.org/10.1109/access.2024.3507925 |
| dc.rights.none.fl_str_mv | Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
| dc.subject.none.fl_str_mv | Dental imaging Dental informatics Deep learning Machine learning Orthopantomography Transfer learning Vision transformer |
| dc.title.none.fl_str_mv | A Hybrid Transfer Learning Approach to Teeth Diagnosis Using Orthopantomogram Radiographs |
| dc.type.none.fl_str_mv | Peer-Reviewed Published version info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| 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 | aus_c1d0b92919cc6e4a1286c30f6ef4fbf4 |
| identifier_str_mv | Alabd-Aljabar, A., Raisan, Z., Adnan, M., & Dhou, S. (2024). A Hybrid Transfer Learning Approach to Teeth Diagnosis Using Orthopantomogram Radiographs. IEEE Access, 12, 178142–178152. https://doi.org/10.1109/access.2024.3507925 2169-3536 10.1109/access.2024.3507925 |
| language_invalid_str_mv | en |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/26274 |
| publishDate | 2024 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
| spelling | A Hybrid Transfer Learning Approach to Teeth Diagnosis Using Orthopantomogram RadiographsAlabd-Aljabar, AhmedRaisan, ZainAdnan, MohammedDhou, SalamDental imagingDental informaticsDeep learningMachine learningOrthopantomographyTransfer learningVision transformerThe 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.Open Access Program from the American University of SharjahIEEE2025-08-21T09:29:40Z2025-08-21T09:29:40Z2024-11-28Peer-ReviewedPublished versioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfAlabd-Aljabar, A., Raisan, Z., Adnan, M., & Dhou, S. (2024). A Hybrid Transfer Learning Approach to Teeth Diagnosis Using Orthopantomogram Radiographs. IEEE Access, 12, 178142–178152. https://doi.org/10.1109/access.2024.35079252169-3536https://hdl.handle.net/11073/2627410.1109/access.2024.3507925enhttps://doi.org/10.1109/access.2024.3507925Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/oai:repository.aus.edu:11073/262742025-08-21T15:29:18Z |
| spellingShingle | A Hybrid Transfer Learning Approach to Teeth Diagnosis Using Orthopantomogram Radiographs Alabd-Aljabar, Ahmed Dental imaging Dental informatics Deep learning Machine learning Orthopantomography Transfer learning Vision transformer |
| status_str | publishedVersion |
| 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 | A Hybrid Transfer Learning Approach to Teeth Diagnosis Using Orthopantomogram Radiographs |
| topic | Dental imaging Dental informatics Deep learning Machine learning Orthopantomography Transfer learning Vision transformer |
| url | https://hdl.handle.net/11073/26274 |