Oral cancer classification from hybrid ABC-PSO and Bayesian LDA
Harikumar Rajaguru, Sunil Kumar Prabhakar · 2017
For both men and women, oral cancer can occur accompanied with severe other health problems. If the oral cancer is detected at an early stage it can save the life of the patient but if detected at a later stage, it is very difficult to save the patient. Despite of advancing techniques in radiation therapy, chemotherapy and surgery, the mortality rate is too high in the case of oral cancer. Therefore early detection and classification of oral cancer is quite important. In this paper, Hybrid Artificial Bee Colony — Particle Swarm Optimization (ABC-PSO) algorithm and Bayesian Linear Discriminant Analysis (BLDA) is utilized to classify the risk level of oral cancer. The results show that when Hybrid ABC-PSO classifier is used, a classification accuracy of 100% is obtained while for BLDA classifier, a classification accuracy of about 83.16% is obtained.