Detection and Identification of Dental Caries Using Segmentation Techniques
DOI:
https://doi.org/10.30526/38.3.4099Keywords:
Image Processing, Machine learning, Teeth detection, Dental caries, Segmentation, Medical Image Segmentation, U-NetAbstract
Dental caries, also named tooth decay, is a major issue for oral health and is caused by bacteria in dental plaque. Detecting caries early on is essential for preventing further damage. Because caries are often small, they can lead to unnecessary treatments or missed diagnoses. This study tackles the challenge of spotting dental caries using color image analysis. We tested both traditional methods—like Quickshift, Simple Linear Iterative Clustering (SLIC) superpixels, and k-means clustering—combined in the Multi-Step Segmentation with K-Means (MSS-KM) approach, as well as a more advanced deep learning method using YOLOv12 for segmentation. The evaluation of the performance of these methods based on accuracy, precision, recall, mean average precision (mAP), and F1-score. The results were impressive, showing that YOLOv12 clearly outperforms MSS-KM in terms of accuracy. YOLOv12 achieved an accuracy of 98.12%, while MSS-KM was at 96.79%. In addition to accuracy, YOLOv12 had excellent precision (99.6%), recall (98.1%), and an F1-score of 0.99, while MSS-KM came in at 88.5% for precision, 91% for recall, and an F1-score of 89.1%. YOLOv12 also had a mAP of 99.5%, compared to MSS-KM's 99.3%. These results clearly show that YOLOv12 is more accurate and reliable for detecting dental caries than MSS-KM. While the MSS-KM method still has value, particularly for traditional segmentation techniques, the model showed strong potential for practical use in clinical settings. The consistent training setup contributed to stable performance, while the comparison with traditional methods highlighted how modern deep learning approaches can significantly enhance diagnostic accuracy. These results not only support the use of YOLOv12 for early caries detection but also suggest that such AI models could become valuable tools in improving patient outcomes and reducing unnecessary treatments
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