  Large medical image data sets with high dimensionality require substantial
amount of computation time for data creation and data processing. This paper
presents a novel generalized method that finds optimal image-based feature sets
that reduce computational time complexity while maximizing overall
classification accuracy for detection of diabetic retinopathy (DR). First,
region-based and pixel-based features are extracted from fundus images for
classification of DR lesions and vessel-like structures. Next, feature ranking
strategies are used to distinguish the optimal classification feature sets. DR
lesion and vessel classification accuracies are computed using the boosted
decision tree and decision forest classifiers in the Microsoft Azure Machine
Learning Studio platform, respectively. For images from the DIARETDB1 data set,
40 of its highest-ranked features are used to classify four DR lesion types
with an average classification accuracy of 90.1% in 792 seconds. Also, for
classification of red lesion regions and hemorrhages from microaneurysms,
accuracies of 85% and 72% are observed, respectively. For images from STARE
data set, 40 high-ranked features can classify minor blood vessels with an
accuracy of 83.5% in 326 seconds. Such cloud-based fundus image analysis
systems can significantly enhance the borderline classification performances in
automated screening systems.
