| Abstract |
Dental radiolucencies appear as darker regions in X-ray images, indicating reduced tissue density, which can suggest conditions ranging from benign lesions to serious pathology. Radiolucencies are broadly categorized into intraosseous (within the tooth structure) and periapical (in surrounding bone), each type exhibiting distinct morphological features. My project employs machine learning, specifically integrating active contour models and fuzzy logic, to streamline the classification and analysis of these radiolucent areas.
Active contour models assist in the segmentation of X-ray images by dynamically outlining regions of interest, a crucial step for isolating radiolucent areas accurately. Fuzzy logic, designed to handle imprecision in medical imaging, improves the algorithm’s ability to classify areas with overlapping or ambiguous boundaries, enhancing diagnostic accuracy.
Using reinforcement learning principles, I developed a neural network-based classification algorithm, which I rigorously coded and tested. Training involved supervised learning with labelled datasets, allowing the model to iteratively optimize for increased accuracy. Once validated, the algorithm was deployed through two website prototypes, enabling accessible, real-world application of the tool for healthcare professionals.
Machine learning-based classification not only expedites radiolucency analysis but also handles large datasets with a consistency and efficiency that would be challenging for manual assessment. This project applies advanced machine learning methodologies and holds potential as a diagnostic aid, allowing dental professionals to better identify and evaluate radiolucent regions, ultimately supporting more timely and precise patient care.
|