Decision support for automated screening of diabetic retinopathy

Diabetic retinopathy (DR) is the leading cause of blindness. DR results in retinal disorders that include: microaneurysms, drusens, hard exudates and intra-retinal micro-vascular abnormalities (IRMA). The early signs of DR are depicted by microaneurysms among other signs. A prompt diagnosis when the disease is at the early stage can help prevent irreversible damages to the diabetic eye. This paper presents a decision support framework for automated screening of early signs of DR and classification schemes for deducing the presence or absence of microaneurysms are developed and tested under a univariate environment. The detection rule is based on binary hypothesis testing problem which simplifies the problem to yes/no decisions. An analysis of the performance of the Bayes optimality criteria is also presented in the paper.