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- 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 SJRQ1MobileNet- and attention-based MLP-Mixer architecture for geographical-region recognition of a marine fish (Perciformes: Carangidae) using otolith images
Dürrani, Ömerhan; İşgüzar, Seda; İmak, Andaç; Ateşşahin, Tuncay; Cömert, Zafer; Dürrani, Syeda Zahra; Türkoğlu, Muammer
Fishery management is crucial to sustain marine ecosystems by preventing overfishing and ensuring a fair distribution of fishing quotas. Accurately identifying the geographical origins of fish stocks is a key challenge in region-specific management strategies. Otoliths, calcified structures found in the heads of all fish species (except sharks and rays), provide insights into the life history and geographical origins of these fish. Traditional otolith analysis is time-consuming and error-prone because of manual inspection. Our study presents a novel approach using deep learning and computer vision to automate the geographical recognition of fish using otolith images. We propose a model that integrates MobileNet, which is known for its efficiency, with an advanced Mlp-Mixer that incorporates an attention mechanism to extract enhanced features. When tested on a diverse dataset of otolith images from five regions, the proposed model achieved a remarkable 96% accuracy, significantly outperforming traditional methods. This high accuracy demonstrates the potential to revolutionise fishery management by providing a fast, reliable, and automated solution for geographical region identification. In conclusion, the proposed method demonstrates the transformative potential of combining MobileNet and an attention-based Mlp-Mixer for automated fish geographic recognition using otolith images. This innovative method addresses the limitations of traditional manual inspection and paves the way for more effective and sustainable fishery management practices.
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