Classification of Large-Scale Fundus Image Data Sets: A Cloud-Computing Framework
This work addresses the problem of efficient automated screening for diabetic retinopathy in medical imaging, offering incremental improvements in speed and accuracy through cloud-based methods.
The paper tackles the computational challenge of processing large fundus image datasets by proposing a cloud-computing framework that finds optimal feature sets to reduce time complexity while maximizing classification accuracy for diabetic retinopathy detection, achieving up to 90.1% accuracy in 792 seconds for DR lesions and 83.5% accuracy in 326 seconds for blood vessels.
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.