DETECTION OF ORAL LICHEN PLANUS USING MACHINE LEARNING TECHNIQUE

Abstract

Oral lichen planus (OLP) is a relatively common oral disorder that shares clinical and histopathological features with other lichen planus lesions, resulting in significant inter-observer disagreement. This affects our understanding of the pathogenesis of this disease and its potential for malignant transformation. Artificial intelligence creates machine learning artificial neural networks to detect diseases. In the proposed work, the microscopic image was taken. Features extraction techniques mainly Block Intensity Code Comparison and Histogram were used to extract the features. The derived features were fed into Support Vector Machine and Gaussian Mixture Models which is then classified into normal and abnormal. From the above experiments, when compared with support vector machine and Gaussian Mixture Model gives the satisfactory results of 95.00%.