Comparing Conventional and Deep Feature Models for Classifying Fundus Photography of Hemorrhages
This work addresses the challenge of identifying hemorrhages for improved diagnosis of diabetic retinopathy, but it is incremental as it compares existing feature types without introducing a new paradigm.
The study tackled the problem of detecting hemorrhages in diabetic retinopathy fundus images by comparing conventional and deep feature extraction methods, finding that deep models were more effective for classification.
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.