AI-Powered Classification of Oral Lesions: Improving Early Detection and Diagnosis

Authors : Hakan Yılmaz, Mehmet Özdem

Pages : 151-158

View : 27 | Download : 22

Publication Date : 2024-12-22

Article Type : Research Paper

Abstract :Oral malignancies pose significant global health challenges, with oral squamous cell carcinoma (OSCC) being the most prevalent form. Early detection of potentially malignant oral disorders (OPMDs) such as leukoplakia and oral submucous fibrosis is crucial for improving patient prognosis. Traditional diagnostic approaches often face limitations like subjective interpretation and potential delays. This study aimed to develop and evaluate a deep learning-based model for the classification of oral lesions as benign or malignant using publicly available image datasets. Utilizing a modified VGG16 architecture and optimized preprocessing techniques, the model was trained on 330 annotated intraoral images and achieved an overall accuracy of 94.79%. Key performance metrics included a precision of 95.11%, sensitivity and specificity of 94.58%, and an F1 score of 94.74%. The model’s performance was comparable to or exceeded existing models with larger datasets, demonstrating its capability for effective feature extraction and reliable classification. The high area under the curve (AUC) value of 0.96 reinforced its potential for clinical application. While the model showed strong diagnostic capability, expanding the dataset size and incorporating a broader range of cases could further enhance generalizability. Future work should also consider integrating real-time image acquisition and optimizing computational processes for practical deployment. The findings underscore the promise of AI-driven diagnostic tools in supporting healthcare professionals by enabling timely, accurate, and scalable detection of oral malignancies, thereby contributing to improved patient care and outcomes. This study represents a significant step toward the practical application of AI in oral health diagnostics.
Keywords : OSCC tespiti, Oral lezyonlar, OPMD sınıflandırması, VGG16 modeli