A new linear adaptive swarm intelligence approach using back propagation neural network for dental caries classification

摘要

The process of analyzing the dental caries from the x-ray images is quite challenging in the medical field. For the purpose of avoiding the approximation in deciding the diagnosis for caries affected tooth. Here the proposed work is introduced with the enhanced technique for analyzing the caries with newly generated linearly adaptive method in particle swarm optimization for Feed Forward Neural Network. This technique is concentrated on binary classification approach, and this inductive attempt shows the connotation of modified Linearly Adaptive Particle Swarm Optimization [LA-PSO] is fused with Back Propagation Neural Network for the classification of good and caries affected tooth from the feature extracted of an individual image, using grey level cooccurrence matrix [GLCM] for the periapical dental x-ray images, which is acquired from the panoramic x-ray images. With that the proposed technique achieved the accuracy of about 99 % with minimized testing [MSE] mean squared error rate of about 0.008.