  Diabetic retinopathy is an eye-related pathology creating abnormalities and
causing visual impairment, proper treatment of which requires identifying
irregularities. This research uses a hemorrhage detection method and compares
classification of conventional and deep features. Especially, method identifies
hemorrhage connected with blood vessels or reside at retinal border and
reported challenging. Initially, adaptive brightness adjustment and contrast
enhancement rectify degraded images. Prospective locations of hemorrhages are
estimated by a Gaussian matched filter, entropy thresholding, and morphological
operation. Hemorrhages are segmented by a novel technique based on regional
variance of intensities. Features are then extracted by conventional methods
and deep models for training support vector machines, and results evaluated.
Evaluation metrics for each model are promising, but findings suggest that
comparatively, deep models are more effective than conventional features.
