ENHANCED DENTAL DISEASE DIAGNOSIS USING YOLO11 AND AN INTEGRATED DATASET
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Panoramic radiographs, Radiographic images, Integrated dataset, Deep learning, Object detection, Real image dataset, Dental diseases, YOLO
Abstract
Detection of dental diseases is a crucial initial step in dental treatment’s planning. The most common dental diseases are cavity, filling, impacted and implants. Accurate diagnosis is the most important aspect of dental diseases’ treatment. Various deep learning (DL) models are used for the detection of dental diseases. Panoramic radiographic images are used widely for detecting dental cavity. The overall performance of the diagnostic models that use panoramic radiographic images for the cavity detection remains significantly challenging. This paper utilizes the Dental Radiography Dataset (DRD), the Real Image Dataset (RID), and a proposed-preprocessed-integrated dataset (the DRD is merged with the RID) in YOLO11’s DL model. When YOLO11 trained on the DRD, it provided detection enhancement for all classes. However, the cavity detection remained low. The cavity class was then removed from the DRD, and the RID was replaced to overcome the problem of low detection. The DRD consisted of 1269 images. The RID consisted of 3340 images after augmentation (flip, rotation, and zoom). The mean average precision (mAP) when the intersection over union (IOU) is greater than or equal to 0.5 (mAP@50) is determined to evaluate the performance of the model. The YOLO11 with DRD achieved mAP@50 of 83.7%, and after removing the cavity class from DRD, mAP@50 reached 93.6%. The YOLO11, when trained with the RID, obtained mAP@50 of 85.8%, and post-augmentation, it increased to 89.9%. The integrated dataset produced mAP@50 of 90.4%. The preprocessed integrated dataset demonstrated superior detection performance compared to results in previous studies.
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References
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