Diagnosing Gingiva Disease Using Artificial Intelligence Techniques
Gingival and periodontal diseases, such as gingivitis and periodontitis, are critical public health concerns that can lead to severe complications if left untreated. Early and precise diagnosis is crucial to mitigate the progression of these conditions and improve oral health outcomes. This study i...
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| Format: | Article |
| Language: | English |
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University of Diyala
2025-06-01
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| Series: | Diyala Journal of Engineering Sciences |
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| Online Access: | https://djes.info/index.php/djes/article/view/1595 |
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| author | Rana Khalid Sabri Lujain Younis Abdulkadir AbdulSattar Mohammed Khidhir Hiba Abdulkareem Saleh |
| author_facet | Rana Khalid Sabri Lujain Younis Abdulkadir AbdulSattar Mohammed Khidhir Hiba Abdulkareem Saleh |
| author_sort | Rana Khalid Sabri |
| collection | DOAJ |
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Gingival and periodontal diseases, such as gingivitis and periodontitis, are critical public health concerns that can lead to severe complications if left untreated. Early and precise diagnosis is crucial to mitigate the progression of these conditions and improve oral health outcomes. This study investigates the application of convolutional neural networks (CNNs) in diagnosing gingival diseases using medical images, including X-rays and intraoral photographs. Several CNN architectures, including VGG16, Sequential CNN, MobileNet, InceptionV3, and suggestions for a voting method to enhance the prediction, were evaluated for their performance in classifying gingival conditions. MobileNet emerged as the most effective model, achieving a test accuracy of 92.73%; the suggested method relies mainly on its positive result. When the MobileNet's result is false, the process takes the voting result using the other methods. This boosts the accuracy to 96%. Surpassing other models in precision and recall metrics. Pre-processing techniques such as normalization using the CIELAB color space and data augmentation significantly enhanced model accuracy. The study employed robust evaluation methods, including 10-fold cross-validation and hyperparameter tuning, to ensure model reliability and generalizability. The findings highlight the transformative potential of AI-powered diagnostic tools in dental healthcare. By leveraging lightweight and efficient architectures like MobileNet, these tools can be deployed in resource-limited settings, offering real-time diagnostic support to healthcare professionals. Future work will focus on expanding datasets, exploring ensemble models, and improving interpretability to further enhance diagnostic accuracy and clinical applicability.
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| format | Article |
| id | doaj-art-2f189a7058dd4b0ca8892b94a08e6a87 |
| institution | Kabale University |
| issn | 1999-8716 2616-6909 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | University of Diyala |
| record_format | Article |
| series | Diyala Journal of Engineering Sciences |
| spelling | doaj-art-2f189a7058dd4b0ca8892b94a08e6a872025-08-20T03:27:40ZengUniversity of DiyalaDiyala Journal of Engineering Sciences1999-87162616-69092025-06-0118210.24237/djes.2024.18211Diagnosing Gingiva Disease Using Artificial Intelligence TechniquesRana Khalid Sabri0Lujain Younis Abdulkadir1AbdulSattar Mohammed Khidhir2Hiba Abdulkareem Saleh3Department of Cyber Security and Cloud Computing Technology Engineering, Northern Technical University, Mosul, Iraq Department of Networks and Computer Software Techniques, Northern Technical University, Mosul, Iraq Department of Networks and Computer Software Techniques, Northern Technical University, Mosul, Iraq Chemical and Petroleum Industries Technology Engineering, Northern Technical University, Mosul, Iraq Gingival and periodontal diseases, such as gingivitis and periodontitis, are critical public health concerns that can lead to severe complications if left untreated. Early and precise diagnosis is crucial to mitigate the progression of these conditions and improve oral health outcomes. This study investigates the application of convolutional neural networks (CNNs) in diagnosing gingival diseases using medical images, including X-rays and intraoral photographs. Several CNN architectures, including VGG16, Sequential CNN, MobileNet, InceptionV3, and suggestions for a voting method to enhance the prediction, were evaluated for their performance in classifying gingival conditions. MobileNet emerged as the most effective model, achieving a test accuracy of 92.73%; the suggested method relies mainly on its positive result. When the MobileNet's result is false, the process takes the voting result using the other methods. This boosts the accuracy to 96%. Surpassing other models in precision and recall metrics. Pre-processing techniques such as normalization using the CIELAB color space and data augmentation significantly enhanced model accuracy. The study employed robust evaluation methods, including 10-fold cross-validation and hyperparameter tuning, to ensure model reliability and generalizability. The findings highlight the transformative potential of AI-powered diagnostic tools in dental healthcare. By leveraging lightweight and efficient architectures like MobileNet, these tools can be deployed in resource-limited settings, offering real-time diagnostic support to healthcare professionals. Future work will focus on expanding datasets, exploring ensemble models, and improving interpretability to further enhance diagnostic accuracy and clinical applicability. https://djes.info/index.php/djes/article/view/1595InceptionV3MobileNetPeriodontal DiseasesSequentialVGG16 |
| spellingShingle | Rana Khalid Sabri Lujain Younis Abdulkadir AbdulSattar Mohammed Khidhir Hiba Abdulkareem Saleh Diagnosing Gingiva Disease Using Artificial Intelligence Techniques Diyala Journal of Engineering Sciences InceptionV3 MobileNet Periodontal Diseases Sequential VGG16 |
| title | Diagnosing Gingiva Disease Using Artificial Intelligence Techniques |
| title_full | Diagnosing Gingiva Disease Using Artificial Intelligence Techniques |
| title_fullStr | Diagnosing Gingiva Disease Using Artificial Intelligence Techniques |
| title_full_unstemmed | Diagnosing Gingiva Disease Using Artificial Intelligence Techniques |
| title_short | Diagnosing Gingiva Disease Using Artificial Intelligence Techniques |
| title_sort | diagnosing gingiva disease using artificial intelligence techniques |
| topic | InceptionV3 MobileNet Periodontal Diseases Sequential VGG16 |
| url | https://djes.info/index.php/djes/article/view/1595 |
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