Multi-level Quality Assessment of Retinal Fundus Images using Deep Convolution Neural Networks

Retinal fundus image quality assessment is one of the major steps in screening for retinal diseases, since the poor-quality retinal images do not allow an accurate medical diagnosis. In this paper, we first introduce a large multi-level Retinal Fundus Image Quality Assessment (RFIQA) dataset. It has six levels of quality grades, which are based on important regions to consider for diagnosing diabetic retinopathy (DR), Aged Macular Degeneration (AMD) and Glaucoma by ophthalmologists. Second, we propose a Convolution Neural Network (CNN) model to assess the quality of the retinal images with much fewer parameters than existing deep CNN models and finally we propose to combine deep and generic texture features, and using Random Forest classifier. Experiments show that combing both deep and generic features outperforms using any of the two feature types in isolation. This is confirmed on our new dataset as well as on other public datasets.