<![CDATA[Inferior Alveolar Nerve Injury (IANI) is a severe complication in oral surgery that can significantly affect a patient's quality of life. Accurate diagnosis is crucial for effective management, and digital radiography has become an essential tool in this regard. This study proposes a novel feature selection-based classification algorithm to enhance the diagnostic precision of digital radiographs (DRs) for IANI detection. The objective is to improve classification accuracy by selecting the most relevant features using a Firefly algorithm-based method. Our approach identifies optimal features that preserve critical information from the dataset, enabling more accurate predictions by machine learning models. The proposed method was tested using a dataset of 140 DRs and achieved a classification accuracy of 97.4%, with a sensitivity of 80.9% and a specificity of 94.8%. These results demonstrate that the Firefly algorithm-based feature selection significantly outperforms traditional methods in diagnosing IANI. The novelty of this research lies in its integration of advanced feature selection techniques with support vector machines, offering a robust tool for improving diagnostic accuracy in dental imaging. This work contributes to enhanced clinical decision-making and could be valuable for broader applications in healthcare systems.]]>
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