Differentiation of benign and malignant oral lesions through surface texture analysis and SVM modeling.

Abstract

Objectives: To evaluate the diagnostic potential of surface texture features extracted from clinical images in objectively differentiating benign from malignant oral lesions, and to validate classification performance of a Support Vector Machine (SVM) model using these features.

Materials and methods: This study included 275 intraoral photographs of oral mucosal lesions with biopsy-confirmed diagnoses, sourced from both institutional archives and a public dataset. Lesion areas were manually annotated and converted into 3D surface plots to extract grayscale-based texture features. Eight statistical descriptors-mean, mode, median, variance, skewness, kurtosis, coefficient of variation (CoV), and entropy-were computed and normalized relative to adjacent healthy mucosa. Group differences were analyzed using MANOVA and effect size metrics (Cohen's d, eta squared). A support vector machine (SVM) with a Gaussian kernel was trained using five-fold cross-validation to classify lesions as benign or malignant based on the extracted features.

Results: Statistical analysis revealed significant differences between benign and malignant groups for all features except skewness (p < 0.001). Entropy, kurtosis, and CoV showed the largest effect sizes, with entropy notably higher in malignant lesions and kurtosis higher in benign ones. The SVM model achieved a sensitivity of 99.2%, specificity of 81.4%, overall accuracy of 90.5%, and an AUC of 0.939, demonstrating high diagnostic performance in distinguishing malignant from benign oral mucosal lesions based on surface texture analysis.

Conclusions: Surface texture features, particularly entropy and kurtosis, offer promising diagnostic indicators for distinguishing malignant from benign lesions. SVM classifier demonstrated robust performance using these parameters.

Clinical relevance: This study highlights surface texture as an objective, underexplored diagnostic parameter. Integrating surface topography into clinical assessments and AI-based tools may enhance early detection and diagnostic accuracy in oral cancer screening.