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    Öğe
    Diagnosis of approximal caries in children with convolutional neural networks based detection algorithms on radiographs: A pilot study
    (Medical Journal Sweden AB, 2025) Yavşan, ZS; Orhan, H; Efe, E; Yavşan, E
    Objectives: Approximal caries diagnosis in children is difficult, and artificial intelligence-based research in pediatric dentistry is scarce. To create a convolutional neural network (CNN)-based diagnostic system for the prompt and efficient identification of approximal caries in pediatric patients aged 5-12 years. Materials and methods: Pediatric patients'digital periapical radiographic images were collected to create a unique dataset. Various augmentation methods were used, and approximal caries in the augmented images were labeled by a pediatric dentist to minimize labeling errors. The dataset consisted of 830 data labeled for approximal caries on 415 images, which were divided into 80% training and 20% testing sets. After comparing 13 detection algorithms, including the latest YOLOv8, the most appropriate one was selected for the proposed system, which was then evaluated based on various performance metrics. Results: The proposed detection system achieved a precision of 91.2%, an accuracy of 90.8%, a recall of 89.3%, and an F1 value of 90.24% after 300 iterations, utilizing a learning rate of 0.01. Conclusion: Approximal caries has been successfully detected with the developed system. Future efforts will focus on augmenting the dataset and expanding the sample size to enhance the efficacy of the system.
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    Öğe
    High-fidelity steganography in EEG signals using advanced transform-based methods
    (PEERJ INC, 2025) Efe, E
    The increasing prevalence of digital health solutions and smart health devices (SHDs) ensures the continuity of personal biometric data while simultaneously raising concerns about their security and privacy. Consequently, the development of novel encryption techniques and data protection policies is crucial to comply with regulations such as The Health Insurance Portability and Accountability Act (HIPAA) and to safeguard against cyber threats. This study introduces a robust and efficient method for embedding private information into electroencephalogram (EEG) signals by employing the stationary wavelet transform (SWT), singular value decomposition (SVD), and tent map techniques. The proposed approach aims to increase embedding capacity while maintaining signal integrity, ensuring resilience against various forms of distortion, and achieving computational efficiency. Experiments were conducted on three publicly available EEG datasets (Graz A, DEAP, and Bonn), and performance was evaluated using widely recognized metrics, including peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), percentage root mean square difference (PRD), normalized cross-correlation (NCC), bit error rate (BER), and Euclidean distance (ED). The results indicate that the method preserves perceptual quality, achieving PSNR values above 60 dB and demonstrating minimal signal distortion. Robustness tests involving noise addition, random cropping, and low-pass filtering confirm the method's high resilience, with BER approaching zero and NCC near unity. Moreover, the proposed method demonstrates significantly reduced hiding and extraction times compared to conventional approaches, enhancing its suitability for real-time, secure biomedical data transmission.