Abstract:
This research introduces a Dental Caries Detection System implemented in Python, leveraging image processing and machine learning techniques to enhance the early diagnosis of dental caries and promote proactive oral health management. The system employs Python’s image processing libraries, such as OpenCV, and machine learning frameworks, such as TensorFlow or PyTorch, to analyze dental radiographs or images for the presence of carious lesions.
The Dental Caries Detection System utilizes advanced image processing algorithms to preprocess and enhance dental images, highlighting potential areas of interest. Machine learning models, trained on diverse datasets of annotated dental images, are employed to classify and localize dental caries with high accuracy. The system also incorporates deep learning architectures to automatically learn intricate patterns indicative of early-stage caries.
Key features of the system include a user-friendly interface for uploading dental images, real-time analysis, and a detailed output report indicating the presence and severity of dental caries. The integration of machine learning enables the system to adapt and improve its diagnostic accuracy over time as it encounters more data.
The open-source nature of the implementation fosters collaboration and allows for customization to accommodate different dental imaging modalities or specific clinical requirements. The research evaluates the system’s performance through rigorous testing on diverse datasets, comparing its results with expert annotations to validate its accuracy and reliability.
By providing a robust and accessible Dental Caries Detection System in Python, this research aims to contribute to the advancement of preventive dental care, enabling early intervention and treatment for individuals at risk of dental caries. The system serves as a valuable tool for dental practitioners, aiding in efficient and accurate diagnosis for improved oral health outcomes.