A fusion-based clinical decision support for disease diagnosis from endoscopic images.

This paper presents an intelligent decision support system designed on a decision fusion framework coupled with a priori knowledge base for abnormality detection from endoscopic images. Sub-decisions are made based on associated component feature sets derived from the endoscopic images and predefined algorithms, and subsequently fused to classify the patient state. Bayesian probability computations are employed to evaluate the accuracies of sub-decisions, which are utilized in estimating the probability of the fused decision. The overall detectability of abnormalities by using the proposed fusion approach is improved in terms of detection of true positive and true negative conditions when compared with corresponding results from individual methods.