GRIFD: A Graded Region-Wise Dissection and Cross-Pooling RNN Framework for Precise Diabetic Retinopathy Detection in Fundus Images

Early and precise detection of diabetic retinopathy (DR) is essential in averting vision impairment. This work introduces graded region wise inspection and feature dissection(GRIFD), a novel deep learning architecture designed explicitly for fine-grained diabetic retinopathy (DR) detection from fundus images. GRIFD enforces a graded region-of-interest (ROI) dissection approach, which methodically segments fundus images into diagnostic blocks to promote local lesion visibility. A Recurrent Neural Network (RNN) is used to model sequential dependencies between these ROI blocks, aiming to mimic the progressive development of DR symptoms.