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.