Support vector machines for oral lesion classification

We investigate support vector machines (SVM) in the context of oral lesion classification using digital color images as input. Two common lesions of similar visual appearance to the human observer were evaluated: oral leukoplakia, which is a potentially pre-cancerous lesion, and oral lichenoid reactions (with subclasses of atrophic, plaqueformed and reticular reactions), which are usually harmless lesions. In total, 89% (212 out of 238, 5-fold CV) were correctly classified in a two-class problem (precancerous vs. non-pre-cancerous) and 78% (61 out of 78, hold-out) into four classes (complete classification). The proposed method can be used as a decision support tool in CADx systems for oral lesion classification and detection of potentially pre-cancerous lesions.