Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2026Scopus SJRQ2A Genetic Algorithm-Optimized ConvNeXtV2-YOLOv8 Framework for Dental Caries Detection in Panoramic Radiographs
Sobahi, Nebras; Küçük, Deniz Bora; Kılıç, Kazım; İmak, Andaç; Çelebi, Adalet; Alghamedi, Yazyd; Yazicioglu, Cafer; Türkoğlu, Muammer; Şengür, Abdulkadir
Background/Objectives: Dental caries is one of the most common oral diseases worldwide, and early diagnosis is essential for effective treatment. However, detecting carious lesions in panoramic radiographs is challenging because of low image contrast, anatomical complexity, and overlapping structures. This study aimed to develop an improved object detection framework for dental caries localization in panoramic radiographs. Methods: A modified YOLOv8 architecture was developed by integrating ConvNeXtV2 blocks into the Cross-Stage Partial (C2f) modules to enhance feature extraction and gradient propagation. To improve detection performance and reduce overfitting, key training hyperparameters were optimized using a genetic algorithm. The proposed model was evaluated on a dataset of 474 panoramic dental radiographs annotated by experts using bounding boxes. Results: The optimized model achieved a box precision of 78.4%, a recall of 53.6%, and a mean average precision at 50% intersection-over-union threshold (AP50) of 62.6%. Compared with the baseline YOLOv8 model, the proposed approach improved precision and AP50. Visual and quantitative analyses demonstrated that the ConvNeXtV2-enhanced architecture enabled more accurate localization of carious regions. Conclusions: The results indicate that combining ConvNeXtV2-based architectural enhancement with genetic algorithm-based hyperparameter optimization is an effective strategy for dental caries localization in panoramic radiographs. Although recall remains a limitation, the proposed framework shows potential as a computer-aided diagnostic tool for supporting clinical caries assessment.
- MakaleAçık Erişim2025Scopus SJRQ2Hybrid CNN-Transformer Model for Accurate Impacted Tooth Detection in Panoramic Radiographs
Küçük, Deniz Bora; Imak, Andaç; Özçelik, Salih Taha Alperen; Çelebi, Adalet; Türkoğlu, Muammer; Sengur, Abdulkadir; Koundal, Deepika
Background/Objectives: The integration of digital imaging technologies in dentistry has revolutionized diagnostic and treatment practices, with panoramic radiographs playing a crucial role in detecting impacted teeth. Manual interpretation of these images is time consuming and error prone, highlighting the need for automated, accurate solutions. This study proposes an artificial intelligence (AI)-based model for detecting impacted teeth in panoramic radiographs, aiming to enhance accuracy and reliability. Methods: The proposed model combines YOLO (You Only Look Once) and RT-DETR (Real-Time Detection Transformer) models to leverage their strengths in real-time object detection and learning long-range dependencies, respectively. The integration is further optimized with the Weighted Boxes Fusion (WBF) algorithm, where WBF parameters are tuned using Bayesian optimization. A dataset of 407 labeled panoramic radiographs was used to evaluate the model’s performance. Results: The model achieved a mean average precision (mAP) of 98.3% and an F1 score of 96%, significantly outperforming individual models and other combinations. The results were expressed through key performance metrics, such as mAP and F1 scores, which highlight the model’s balance between precision and recall. Visual and numerical analyses demonstrated superior performance, with enhanced sensitivity and minimized false positive rates. Conclusions: This study presents a scalable and reliable AI-based solution for detecting impacted teeth in panoramic radiographs, offering substantial improvements in diagnostic accuracy and efficiency. The proposed model has potential for widespread application in clinical dentistry, reducing manual workload and error rates. Future research will focus on expanding the dataset and further refining the model’s generalizability.
AtıfScopus36OpenAlex34
İncele - MakaleYalnız künye2024Scopus SJRQ2Maxillary sinus detection on cone beam computed tomography images using ResNet and Swin Transformer-based UNet
Çelebi, Adalet; Imak, Andaç; Üzen, Hüseyin; Budak, Ümit; Türkoğlu, Muammer; Hanbay, Davut; Şengür, Abdulkadir
Objectives: This study, which uses artificial intelligence-based methods, aimed to determine the limits of pathologic conditions and infections related to the maxillary sinus in cone beam computed tomography (CBCT) images to facilitate the work of dentists. Methods: A new UNet architecture based on a state-of-the-art Swin transformer called Res-Swin-UNet was developed to detect the sinus. The encoder part of the proposed network model consists of a pre-trained ResNet architecture, and the decoder part consists of Swin transformer blocks. Swin transformers achieve powerful global context properties with self-attention mechanisms. Because the output of the Swin transformer generates sectorized features, the patch expanding layer was used in this section instead of the traditional upsampling layer. In the last layer of the decoder, sinus diagnosis was conducted through classical convolution and sigmoid function. In experimental works, we used a data set including 298 CBCT images. Results: The Res-Swin-UNet model achieved more success, with a 91.72% F1-score, 99% accuracy, and 84.71% IoU, outperforming the state-of-the-art models. Conclusions: The deep learning-based model proposed in the present study can assist dentists in automatically detecting the boundaries of pathologic conditions and infections within the maxillary sinus based on CBCT images.
AtıfScopus21OpenAlex20İncele - MakaleAçık Erişim2023Scopus SJRQ2ResMIBCU-Net: an encoder–decoder network with residual blocks, modified inverted residual block, and bi-directional ConvLSTM for impacted tooth segmentation in panoramic X-ray images
Imak, Andaç; Çelebi, Adalet; Polat, Onur; Türkoğlu, Muammer; Şengür, Abdulkadir
Objective: Impacted tooth is a common problem that can occur at any age, causing tooth decay, root resorption, and pain in the later stages. In recent years, major advances have been made in medical imaging segmentation using deep convolutional neural network-based networks. In this study, we report on the development of an artificial intelligence system for the automatic identification of impacted tooth from panoramic dental X-ray images. Methods: Among existing networks, in medical imaging segmentation, U-Net architectures are widely implemented. In this article, for dental X-ray image segmentation, blocks and convolutional block structures using inverted residual blocks are upgraded by taking advantage of U-Net’s network capacity-intensive connections. At the same time, we propose a method for jumping connections in which bi-directional convolution long short-term memory is used instead of a simple connection. Assessment of the proposed artificial intelligence model performance was evaluated with accuracy, F1-score, intersection over union, and recall. Results: In the proposed method, experimental results are obtained with 99.82% accuracy, 91.59% F1-score, 84.48% intersection over union, and 90.71% recall. Conclusion: Our findings show that our artificial intelligence system could help with future diagnostic support in clinical practice.
AtıfScopus21OpenAlex23İncele - MakaleYayıncıda açık erişim2022Scopus SJRQ2Dental Material Detection based on Faster Regional Convolutional Neural Networks and Shape Features
Imak, Andaç; Çelebi, Adalet; Türkoğlu, Muammer; Şengür, Abdulkadir
In this paper, a novel approach is proposed to detect the dental materials in dental Panoramic images. In general, dental Panoramic images are examined by the dentists to detect the dental materials of the patients. Examining the dental Panoramic images is a time-consuming procedure and necessitates highly skilled dentists. Thus, computer imaging and decision support systems based on machine learning are in demand for dental applications. In this study, a semi-automatic, image processing and machine learning based approach is proposed for detection of dental materials in dental Panoramic images. More specifically, the dental materials namely dental filling, dental bridge and tooth crown are targeted by the proposed approach. Within the approach developed, the dental Panoramic images used as input are initially pre-processed for noise removal and tooth region is determined using a deep learning approach. To this end, a faster Regional Convolutional Neural Networks (RCNN) method is employed. A tooth region correction procedure is applied after application of faster RCNN to determine the exact location of the tooth region as faster RCNN may produce more tooth regions than one correct tooth region. Graph cut (GC) based image segmentation is applied to segment the tooth region into foreground and background regions. Shape based feature extraction and classification are applied to determine the tooth problem. A dataset has been established in dental faculty hospital of private dental clinics. The proposed approach is simple yet quite effective. In all classification methods, the average accuracy scores were around 90%.
AtıfScopus9WoS7OpenAlex10İncele