Computer-aided diagnosis system: A Bayesian hybrid classification method

A novel method to classify multi-class biomedical objects is presented. The method is based
on a hybrid approach which combines pairwise comparison, Bayesian regression and the
k-nearest neighbor technique. It can be applied in a fully automatic way or in a relevance
feedback framework. In the latter case, the information obtained from both an expert and
the automatic classification is iteratively used to improve the results until a certain accuracy
level is achieved, then, the learning process is finished and new classifications can be automatically
performed. The method has been applied in two biomedical contexts by following
the same cross-validation schemes as in the original studies. The first one refers to cancer
diagnosis, leading to an accuracy of 77.35% versus 66.37%, originally obtained. The second
one considers the diagnosis of pathologies of the vertebral column. The original method
achieves accuracies ranging from 76.5% to 96.7%, and from 82.3% to 97.1% in two different
cross-validation schemes. Even with no supervision, the proposed method reaches 96.71%
and 97.32% in these two cases. By using a supervised framework the achieved accuracy is
97.74%. Furthermore, all abnormal cases were correctly classified.