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

Authors:
Suman K. Mitra;Te-Won Lee;Michael Goldbaum
Affiliations:
Dhirubhai Ambani Institute of Information and Communication Technology, Post Bag no. 4, Near Indroda Circle, Gandhinagar 382009, Gujarat, India;Institute for Neural Computation, University of California, San Diego, CA 92093-0523, USA;Department of Ophthalmology, University of California, San Diego, CA 92093, USA
Venue:
Pattern Recognition Letters - Special issue: Advances in pattern recognition
Year:
2005

Citing 8
Cited 2

On the Optimality of the Simple Bayesian Classifier under Zero-One Loss

Machine Learning - Special issue on learning with probabilistic representations
Bayesian Network Classifiers

Machine Learning - Special issue on learning with probabilistic representations
Efficient Approximations for the MarginalLikelihood of Bayesian Networks with Hidden Variables

Machine Learning - Special issue on learning with probabilistic representations
Adaptive Probabilistic Networks with Hidden Variables

Machine Learning - Special issue on learning with probabilistic representations
Causality: models, reasoning, and inference

Causality: models, reasoning, and inference
Robust Bayes classifiers

Artificial Intelligence
Bayesian Networks and Decision Graphs

Bayesian Networks and Decision Graphs
Learning Bayesian networks from incomplete databases

UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence

A hybrid feature selection approach based on the Bayesian network classifier and rough sets

RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Accurate detection of blood vessels improves the detection of exudates in color fundus images

Computer Methods and Programs in Biomedicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.