Early diagnosis of oral cancer using a hybrid arrangement of deep belief networkand combined group teaching algorithm

Abstract

Oral cancer can occur in different parts of the mouth, including the lips, palate, gums, and inside the cheeks. If not treated in time, it can be life-threatening. Incidentally, using CAD-based diagnosis systems can be so helpful for early detection of this disease and curing it. In this study, a new deep learning-based methodology has been proposed for optimal oral cancer diagnosis from the images. In this method, after some preprocessing steps, a new deep belief network (DBN) has been proposed as the main part of the diagnosis system. The main contribution of the proposed DBN is its combination with a developed version of a metaheuristic technique, known as the Combined Group Teaching Optimization algorithm to provide an efficient system of diagnosis. The presented method is then implemented in the “Oral Cancer (Lips and Tongue) images dataset” and a comparison is done between the results and other methods, including ANN, Bayesian, CNN, GSO-NN, and End-to-End NN to show the efficacy of the techniques. The results showed that the DBN-CGTO method achieved a precision rate of 97.71%, sensitivity rate of 92.37%, the Matthews Correlation Coefficient of 94.65%, and 94.65% F1 score, which signifies its ability as the highest efficiency among the others to accurately classify positive samples while remaining the independent correct classification of negative samples.