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Detection and classification of enhanced periapical lesion images with YOLO algorithms.

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Title: Detection and classification of enhanced periapical lesion images with YOLO algorithms.
Authors: Akalin, Fatma (AUTHOR), Yildiz, Tuğçenur (AUTHOR)
Source: Connection Science. Dec 2025, Vol. 37 Issue 1, p1-26. 26p.
Subjects: Image processing, Periapical diseases, Data augmentation, Clinical decision support systems, Dental radiography, Object recognition algorithms, Artificial intelligence
Abstract: In recent years, artificial intelligence has become a reliable technology in clinical decision support systems with the solutions it offers in the dental field. This success also indicates a significant potential in detecting five different types of lesions on the tooth root through radiographic images. Because the periapical X-ray-taking process may vary depending on the individual's physical, psychological, and mental conditions. Also, environmental parameters may negatively affect the image acquisition process. It is possible to tolerate these disadvantageous situations with artificial intelligence-based algorithms and image processing approaches. In this study, it was planned to in-depth analysis of the periapical lesion data types in the original dataset called Periapical X-rays provided from the Kaggle public database. For this, the original adaptive image processing approach developed by integrating the ABC optimisation algorithm was applied to the dataset for five different lesion types. Then, the enhanced images enriched with the data augmentation approach were trained with YOLOv7, YOLOv8, YOLOv9 and YOLOv10 algorithms. As a result of the training, the enhanced images compared to the original images reached 96% F Criterion thanks to the network architecture of YOLOv8 algorithm. This shows that the YOLOv8 network architecture in enhanced lesion images is more successful. [ABSTRACT FROM AUTHOR]
Copyright of Connection Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Detection and classification of enhanced periapical lesion images with YOLO algorithms.
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  Data: <searchLink fieldCode="AR" term="%22Akalin%2C+Fatma%22">Akalin, Fatma</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yildiz%2C+Tuğçenur%22">Yildiz, Tuğçenur</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Connection+Science%22">Connection Science</searchLink>. Dec 2025, Vol. 37 Issue 1, p1-26. 26p.
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  Data: <searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Periapical+diseases%22">Periapical diseases</searchLink><br /><searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Clinical+decision+support+systems%22">Clinical decision support systems</searchLink><br /><searchLink fieldCode="DE" term="%22Dental+radiography%22">Dental radiography</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+algorithms%22">Object recognition algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink>
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  Label: Abstract
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  Data: In recent years, artificial intelligence has become a reliable technology in clinical decision support systems with the solutions it offers in the dental field. This success also indicates a significant potential in detecting five different types of lesions on the tooth root through radiographic images. Because the periapical X-ray-taking process may vary depending on the individual's physical, psychological, and mental conditions. Also, environmental parameters may negatively affect the image acquisition process. It is possible to tolerate these disadvantageous situations with artificial intelligence-based algorithms and image processing approaches. In this study, it was planned to in-depth analysis of the periapical lesion data types in the original dataset called Periapical X-rays provided from the Kaggle public database. For this, the original adaptive image processing approach developed by integrating the ABC optimisation algorithm was applied to the dataset for five different lesion types. Then, the enhanced images enriched with the data augmentation approach were trained with YOLOv7, YOLOv8, YOLOv9 and YOLOv10 algorithms. As a result of the training, the enhanced images compared to the original images reached 96% F Criterion thanks to the network architecture of YOLOv8 algorithm. This shows that the YOLOv8 network architecture in enhanced lesion images is more successful. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Connection Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/09540091.2025.2522706
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: Periapical diseases
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      – SubjectFull: Data augmentation
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      – SubjectFull: Clinical decision support systems
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      – SubjectFull: Dental radiography
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      – SubjectFull: Object recognition algorithms
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      – SubjectFull: Artificial intelligence
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      – TitleFull: Detection and classification of enhanced periapical lesion images with YOLO algorithms.
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              M: 12
              Text: Dec 2025
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              Y: 2025
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