A Bayesian network based sequential inference for diagnosis of diseases from retinal images

We propose a system that learns from the STARE (STructured Analysis of REtina) database and exploits the experience of ophthalmologists to assist in decision-making regarding the presence or absence of retinal diseases. The developed system automatically detects diseases given a description (a set of manifestations) of a retinal image. The manifestations in the retinal image are usually fed sequentially into the system where the manifestation dependences and order must be learned by the system. We apply naive Bayes classifier which is a simple case of Bayesian network to learn the conditional probabilities and to establish an approximate lookup table for sequential manifestation input. The system interacts with the ophthalmologist in determining the sequence of manifestations for inferring the correct disease. The overall performance of the system is found to be satisfactory and useful by ophthalmologists.