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Methods: We systematically analyze preprocessing methods suitable for dental bitewing radiographs, covering image enhancement, noise reduction, and contrast adjustment. These techniques are strategically chosen to address common challenges in dental radiograph images, including variations in lighting, contrast disparities, and noise fluctuations. We employ optimized algorithms to meet real-time constraints, ensuring efficient model training and inference.
Results: Our study assesses the impact of each preprocessing step on dataset quality and its influence on AI model performance. Practical recommendations are provided to empower researchers and practitioners in creating datasets optimized for dental bitewing radiograph detection tasks, aiming to improve AI model accuracy while adhering to real-time requirements. In addition, a comparative analysis is conducted, evaluating datasets enhanced using conventional methods against the ResNet18 model for the segmentation of bitewing dental images.
Conclusion: This paper serves as a valuable guide for the dental imaging community, offering insights into preprocessing steps that elevate dataset quality for AI-driven dental bitewing radiograph detection. By emphasizing the relevance of real-time performance and providing a comparison with conventional enhancements on the ResNet18 model, we contribute to advancing early diagnosis and enhancing oral healthcare outcomes.
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