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Optimized Feature Extraction and Multi-Class Modeling for Orthodontic Image Classification
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Keywords
Orthodontic image classification
feature extraction
multi-class modeling
machine learning
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Abstract
The classification of orthodontic images plays a pivotal role in clinical diagnosis, treatment planning, and automated orthodontic assessment. This research proposes an optimized feature extraction framework coupled with multi-class modeling to enhance classification accuracy and computational efficiency. The study leverages advanced image processing techniques to extract discriminative features and integrates robust multi-class machine learning models for accurate categorization of orthodontic imaging data. Experimental results demonstrate significant improvements in classification performance, with the framework effectively handling intra-class variations and inter-class similarities inherent in orthodontic datasets. The proposed methodology addresses critical limitations in existing automated systems, offering a scalable solution adaptable to clinical workflows. This work establishes a foundation for future research on intelligent orthodontic diagnostic systems, bridging the gap between computational modeling and clinical applicability.
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